INCOME ALLOCATION METHOD AND APPARATUS

专利摘要:
yield allocation method and apparatus. A method comprises receiving a first signal indicating an aggregate yield measured by an aggregate yield sensor during a measurement interval receiving a second signal indicating a plurality of geo-referenced regions through which a combine head has traversed before the measurement interval, allocate a portion of the aggregate yield to each of at least two geo-referenced regions based on different travel times for crops of different head portions and issue allocations of aggregate yield portions.
公开号:BR102015024555B1
申请号:R102015024555-6
申请日:2015-09-24
公开日:2020-09-29
发明作者:James J. Phelan；Aaron J. Bruns；Niels Dybro；Noel W. Anderson
申请人:Deere & Company；
IPC主号:

专利说明:

FUNDAMENTALS
[001] Some harvesters experience an aggregate yield being harvested across the width of a harvest head. The aggregate yield data assist in crop management. Unfortunately, aggregate income data is often inaccurate. BRIEF DESCRIPTION OF THE DRAWINGS
[002] Figure 1 is a schematic diagram of an example of an aggregate income allocation system.
[003] Figure 2 is a schematic diagram of an example of an aggregate income allocation scheme.
[004] Figure 3 is a flow chart of an example of an aggregate income allocation method.
[005] Figure 4 is a flowchart of another example of an aggregate income allocation method using weightings.
[006] Figure 5 is a schematic diagram of another example of an aggregate income allocation scheme derived from the method of Figure 4.
[007] Figure 6 is a diagram of an example of income map issued by an example of income allocation system using the method of Figure 4.
[008] Figure 7 is a schematic diagram of another example of an aggregate yield allocation system including a harvester example.
[009] Figure 8 is a front view of an example of the combine head in Figure 7.
[0010] Figure 9 is a perspective view of an example of a row head unit in Figure 8.
[0011] Figure 10 is a diagram of an example of a field being harvested by the harvester of Figure 7, indicating arrival times in the feed chamber for grains from different row units.
[0012] Figure 11 is a diagram of an example of an aggregate income allocation scheme.
[0013] Figure 12 is a schematic diagram of an example of an income estimation system.
[0014] Figure 13 is a schematic diagram of a portion of the yield estimation system in Figure 12.
[0015] Figure 14 is a flow chart of an example method for estimating biomass and / or grain yield.
[0016] Figure 15 is a graph illustrating an example of the relationship between a characteristic of sensed power and biomass yield or grain yield.
[0017] Figure 16 is a bottom view of an example of a combine harvester row unit from the crop sensor system in Figure 12.
[0018] Figure 17 is a top perspective view of the row unit in Figure 16.
[0019] Figure 18 is a schematic diagram of another example of a crop sensor system.
[0020] Figure 19 is a schematic diagram of another example of an aggregate income allocation system.
[0021] Figure 20 is the diagram illustrating an example of allocation f of aggregate income to geo-referenced regions based on examples of pre-harvest weighting data.
[0022] Figure 21 is a schematic diagram of an example of an aggregated income allocation weighting system for shadow prevention.
[0023] Figure 21 is a flowchart of an example method for weighting the aggregate income allocation based on weightings of shadow prevention loss.
[0024] Figure 23 is a diagram illustrating an example of a method to determine shade loss for a crop plant.
[0025] Figure 25 is a diagram illustrating another example of a method for determining shade loss for a crop plant.
[0026] Figure 24 is a diagram illustrating the application of an example grid as part of an example method to calculate shade loss for a crop plant.
[0027] Figure 24 is a graph illustrating shade loss determination for a crop plant by adding shade loss from grid elements of the grid in Figure 24 according to a sigmoid function.
[0028] Figure 26 is a flowchart of an example method to generate an improved yield map with weight loss prevention considerations.
[0029] Figure 27 is a schematic diagram of another example of an aggregate income allocation system. DETAILED DESCRIPTION OF EXAMPLES
[0030] Figure 1 schematically illustrates an example of an aggregate income allocation system 20. The aggregate income allocation system 20 allocates aggregate income to a crop, such as grain or other harvested material such as cane stalks, cotton and the like , to different locations or local geo-referenced regions. As will be described here below, the aggregate yield allocation system 20 takes into account different travel times for crops from different portions of a combine head on an aggregate sensor when allocating aggregate yield. As a result, system 20 more precisely allocates aggregate income to different geo-referenced locations or regions.
[0031] The aggregate yield allocation system 20 comprises a combine 22, a machine controller 24, georeferencing system 26, the aggregate yield sensor 28, a display 30, a processor 32 and a memory 34. The combine 22 comprises a machine for harvesting crops. In one implementation, harvester 22 is self-propelled. In another implementation, harvester 22 is towed. Harvester 22 removes portions of plants (cultivation) from the growth medium or field. In one implementation, harvester 22 comprises a containment tank in which the crop is contained. In another implementation, harvester 22 discharges the removed crop to a containment tank in another vehicle or onto the ground for subsequent collection.
[0032] Harvester 22 comprises crop removal portions 40A, 40B and 40C (collectively referred to as crop removal portions 40) and crop conveyors 42A, 42B, 42C (collectively referred to as crop conveyors 42), 44, each one of which is schematically illustrated. Crop removal portions 40 section, lift and / or remove the crop being harvested from the growth medium or field. In one implementation, crop removal 40 portions cut the stem or stalk of a plant carrying the crop to be harvested. In another implementation, crop removal portions 40 separate the crop to be harvested from the feel or plant while the stalk or plant remains in the soil.
[0033] In the illustrated example, crop removal portions 40 are transversely located across a transverse width of the harvester 22. In one implementation, crop removal portions 40 are located across different transverse locations through a harvester head. In other implementations, crop removal portions 40 are located in different transverse grooves in different harvester grooves 22. Although harvester 22 is illustrated as comprising three crop removal portions 40, in other implementations, harvester 22 comprises two portions of crop removal or more than three crop removal portions. For example, in one implementation, the harvester 22 comprises a head having a plurality of row units, each row unit having a crop removal portion that removes the crop being harvested from the soil.
[0034] Cultivation carriers 42 comprise mechanisms that transport crops, once they have been removed or separated from the growth medium or field, to an aggregation site 48 where crops from different portions 40 are aggregated. In one implementation, the aggregation site 48 comprises a feed chamber in which crops harvested through the head are aggregated. In one implementation, due to the different cross-sectional locations of crop removal portions 40, crop conveyors 42 have different lengths. In one implementation, crop conveyors 42 operate at different transport speeds. Or due to the different cross-sectional locations and / or the different transport speeds, cultures from crop removal portions 40 take different periods of time to reach the aggregation site 48.
[0035] In the illustrated example, crop carrier 42A transports crops from crop removal portion 40A to aggregation site 48 at time T1. Cultivation carrier 42B transports crops from crop removal portion 40B to aggregation site 48 at time T2 which is greater than time T1. Cultivation carrier 42C transports crops from crop removal portion 40C to aggregation site 48 at time T3 which is greater than time T2. In some implementations, one or more of the crop carriers 42 transport crops from different crop removal portions 40 in the same time.
[0036] Cultivation transporter 44 transports crops from aggregation site 48 to a final destination for cultivation, whether in a containment tank loaded by harvester 22, a containment tank from another vehicle or a unloading site on the ground for subsequent collection. In one implementation, crop carrier 44 transports the crop being harvested along with another material during the crop separation from the other material. For example, in one implementation, crop carrier 44 transports portions of a plant and grain while the grain is separated from the remaining portions of the plant. In one implementation, the crop carrier 44 transports the crop via one or more thresher devices.
[0037] Machine controller 24 comprises one or more processing units that emit control signals to control the operational settings for the combine 22. For the purposes of this order, the term "processing unit" must mean a processing unit currently or developed in the future that executes instruction sequences contained in a non-transitory computer-readable medium or memory. The execution of the instruction sequences causes the processing unit to perform steps such as generating control signals. The instructions can be loaded into random access memory (RAM) for execution by the processing unit from a read-only memory (ROM), a mass storage device, or some other persistent storage. In other embodiments, rigid wiring circuitry can be used in place of or in combination with software instructions to implement the described functions. For example, machine controller 24 must be embodied as part of one or more application-specific integrated circuits (ASICs). Unless specifically noted to the contrary, the controller is not limited to any specific combination of hardware and software circuitry, nor to any particular source for instructions executed by the processing unit.
[0038] In one implementation, the machine controller 24 controls a translator speed of the combine 22 across a field, a sectioning or removal parameter for each of the crop removal portions 40, a transport parameter or speed for each one of the conveyors 42, 44, and / or operational settings for combine harvester devices 22. In one implementation, machine controller 24 is driven on board by harvester 22. In another implementation, machine controller 24 is at least partially embodied in a remote location, where such control signals are transmitted wirelessly from the remote location to a communication transceiver driven by the combine 22.
[0039] The geo-reference system 26 comprises a device 'by which different regions of a field are identified, labeled and / or geo-referenced to receive attribution of crop yield characteristics. In an implementation, the geo-reference system 26 specifically identifies a particular region or location on the field that is currently being acted upon or traversed by the combine 22. In an implementation, the geo-reference system 26 identifies regions of a field with a resolution such that each individual geo-referenced region has a width substantially equal to the width of the harvester 22, such as the width of a head, such as when the harvester 22 comprises a combination. In another implementation, the resolution is such that each geo-referenced region has a width of a plurality of rows less than the total width of the harvester 22, where the total width of the harvester 22 moves through multiple geo-referenced regions distinctly identified. In yet another implementation, the resolution is such that each geo-referenced region has a width equal to an individual row of plants, with each geo-referenced region having a width corresponding to an individual row of plants. In one implementation, the geo-referencing system resolution 26 identifies geo-referenced regions having a length of a single row of plants, a single plant across multiple rows. In another implementation, the resolution is such that each geo-referenced region has a length of multiple rows of plants, a set of multiple consecutive plants in each row. In one implementation, the geo-reference system 26 comprises an antenna and associated electronics / software as part of a global satellite navigation system (GNSS) or global positioning system (GPS). In other implementations, other devices or other methods and / or technologies are used.
[0040] The aggregate yield sensor 28 comprises one or more sensors along the conveyor 44 that emit a signal indicating an aggregate yield of the crop that was harvested during a measurement interval. In an implementation, an aggregate yield is the aggregation or combined total yield from each of the portions 40 and as sensed by the aggregate yield sensor 28. In an implementation, a measurement interval is a measurement interval during which the amount of crop, resulting in the aggregate yield value, was sensed or detected by the aggregate yield sensor 28. In one implementation, the measurement interval is defined as the lapse of a predetermined amount of time during which the harvester 22 traverses a field . In another implementation, the measurement interval is defined as a distance traveled by the combine 22. During each measurement interval and / or at the end of each measurement interval, the aggregate yield sensor 28 emits signals that indicate a total or cumulative quantity of culture that was sensed during the measurement interval. In an implementation, each measurement interval is the same amount of time or the same amount of distance. In still other implementations, the measurement intervals vary in duration or distance.
[0041] In one implementation, the aggregate yield sensor 28 comprises a grain flow sensor that detects a crop flow, grain, per carrier 44. For example, in one implementation, aggregate yield sensor 28 comprises an attenuation sensor gamma ray measuring the flow of aggregate grain harvested by the conveyor 44. In one implementation, the aggregate yield sensor 28 comprises an impact plate sensor that detects grain impact against a surface or sensor plate in order to measure the flow in aggregate grain mass harvested by the carrier 44. In yet another implementation, the aggregate yield sensor 28 comprises one or more load cells that measure or detect a bulk aggregate harvested grain or load. For example, in one implementation, one or more load cells can be located at the bottom of a containment tank loaded by the harvester 22, where changes in weight or grain mass within the containment tank during the measurement interval indicate the aggregate yield during the measurement interval. In yet another implementation, the aggregate yield sensor 28 comprises cameras or optical sensor devices that detect the size and / or shape of a mass of harvested grain aggregate, such as the shape of the pile or height of a pile of grain in a combine harvester containment tank 22, in which the change in shape or height of the pile during the measurement interval indicates the aggregate yield during the measurement interval. In other implementations, other aggregate-yielding sensing technologies are employed. In some implementations, the aggregate yield sensor 28 comprises two or more of the sensors described above, where the aggregate yield for the measurement interval is determined from signals emitted by each of the multiple different types of sensors. For example, in an implementation, aggregate throughput is determined based on signals from a gamma-ray attenuation sensor, an impact plate sensor, load cells inside a containment tank and optical sensors over a harvester containment tank 22.
[0042] The display 30 comprises a monitor, screen, panel or other device by which information is visibly communicated. In one implementation, the display 30 additionally comprises auditory communication capabilities. Display 30 facilitates the presentation of information by identifying the allocation of aggregate income among different geo-referenced regions. In one implementation, the display 30 is loaded on board the harvester 22 for viewing by an operator on the harvester 22. In another implementation the display 30 is located remote with respect to the harvester 22, such as when the harvester 22 is remotely operated or such as when managers or remote personnel are analyzing or reviewing aggregate income from different geo-referenced regions in a field.
The processor 32 comprises one or more processing units that receive signals from the aggregate yield sensor 28 and signals from the geo-referencing system 26 and uses such signals to allocate aggregate yield between different geo-referenced regions. In one implementation, is embodied as part of machine controller 24. In another implementation, processor 32 is separate and independent from machine controller 24.
[0044] Memory 34 comprises a computer-readable medium - non-transitory or persistent storage device. In one implementation, memory 34 is loaded by harvester 22. In another implementation, memory 34 is remote from harvester 22. In yet another implementation, memory 34 is distributed across different locations. Memory 34 comprises an aggregate yield module 50, a yield allocation module 52, a machine control module 54, a yield mapping module 56 and data storage 58.
[0045] The aggregate performance module 50 comprises software, code, circuitry and / or program logic providing instructions for directing the processor 32 to determine an aggregate performance for each measurement interval based on signals received from the aggregate performance sensor 28. As noted above, the aggregate yield for each measurement interval is based on signals received from a gamma-attenuation sensor, an impact plate sensor, flow sensors, load sensors and / or optical sensors.
[0046] Yield allocation module 52 comprises software, code, circuitry and / or program logic providing instructions for directing processor 32 to allocate portions of aggregate yield over a particular measurement range for each of at least two geo regions -references that were traversed by the harvester 22 before the particular measurement interval, where the allocation is based on different amounts of time for crops to shift to the aggregate yield sensor 28 after being initially separated from the growth medium or soil to the aggregate yield sensor 28. In the example shown, the time for crops to move from aggregation site 48 to aggregate yield sensor 28 is the same for crops harvested from each of the 40 portions. However, due to or the different travel distances and / or different transport speeds, reflected by the different travel times T1, T2 and T3 even portions 40A, 40B and 40C, respectively, crops removed by crop removal portions 40A, 40B and 40C during a particular measurement interval arrive at the aggregate yield sensor 28 at different times after the completion of the measurement interval. Yield allocation module 52 allocates the aggregate yield value for the measurement range to different geo-referenced regions that were traversed or interacted with crop removal portions 40 prior to the measurement range.
[0047] Figure 2 illustrates schematically an example of an aggregate yield allocation scheme 100 performed by the aggregate yield allocation system 20. Figure 2 illustrates the initial removal of cultivation from the growth medium or field by each of the portions crop removal 40 during each of the measurement intervals 1, 2 and 3. The straight arrows in Figure 2 indicate transport of the removed crop to the aggregate yield sensor 28, where the end of such straight arrows indicates the measurement interval during which the removed crop arrives at the aggregate yield sensor 28 and contributes to a given aggregate yield for an associated measurement range. The curved allocation arrows in Figure 2 indicate allocation of aggregate yield from each particular measurement interval for geo-referenced regions that were traversed by the combine 22 during previous measurement intervals.
[0048] As shown in Figure 2, the crop removal portion 40A removes culture from the growth medium at time 60 during measurement interval 1. After being removed, the crop removed at time 60A is transported, and possibly still interacted as being threshed to separate from other portions of the plant, until cultivation is sensed by the aggregate yield sensor 28 at time 62A which occurs during a subsequent measurement interval 3. The difference between time 60A and 62A is the time consumed as a crop moves from portion 40A to aggregate yield sensor 28, times T1 + T4, where TI time is a duration of time for cultivation to travel from crop removal portion 40A for aggregation site 48 and where time T4 is a length of time for cultivation to travel from aggregation site 48 to aggregate yield sensor 28.
[0049] Likewise, the crop removal portion 40B removes culture from the growth medium at time 60B during measurement interval 1. After being removed, the crop removed at time 60B is transported, and possibly still interacted as being threshed to separate from other portions of the plant, until cultivation is sensed by the aggregate yield sensor 28 at time 62B which occurs during a subsequent measurement interval 4. The difference between time 60B and 62B is the time consumed as a crop moves from portion 40B to the aggregate yield sensor 28, times T2 + T4, where time T2 is a length of time for cultivation to travel from crop removal portion 40B to the aggregation site 48 and where time T4 is a duration of time for the crop to travel from the aggregation site 48 to the aggregate yield sensor 28. Since the travel time T2 is longer than the travel time Tl, cultivations ap Artir from portion 40B contributes to the aggregate yield of measuring range 4 instead of measuring range 3 although the cultures for portions 40A and 40B were both separated from the growth medium during the same measuring range 1.
[0050] Likewise, the 40C crop removal portion removes culture from the growth medium at 60C time during measurement interval 1. After being removed, the 60C time removed culture is transported, and possibly still interacted as being threshed to separate grain from other portions of the plant, until cultivation is sensed by an aggregate yield sensor 28 in time 62C which occurs during a subsequent measurement interval 5. The difference between 60C and 62C times is the time consumed as a crop moves from portion 40C to the aggregate yield sensor 28, times T3 + T4, where time T3 is a duration of time for cultivation to travel from crop removal portion 40C to aggregation site 48 and where time T4 is a duration of time for the crop to travel from the aggregation site 48 to the aggregate yield sensor 28. Since the travel time T3 is longer than the travel time T2, cultivations ap portion 40C contributes to the aggregate yield of measurement range 5 rather than measurement range 4 although the cultures for portions 40B and 40C were both separated from the growth medium during the same measurement range 1.
[0051] As indicated by the curved allocation arrows in Figure 2, the yield allocation module 52 allocates portions of aggregate yield for each particular measurement interval back to two or more geo-referenced regions traversed by the harvester 22 before the particular measurement. In the illustrated example, the aggregate yield for measurement interval 5, as determined by the aggregate yield module 50, is allocated back to the geo-referenced region 70 (as indicated by arrow 71 A), which was traversed by the harvester 22 during the measuring range 3; it is allocated back to the geo-referenced region 72 (as indicated by arrow 7IB), which was traversed by the harvester 22 during measurement interval 2; and is allocated back to the geo-referenced region 74 (as indicated by arrow 71C), which was traversed by the combine during measurement interval 1.
[0052] The yield allocation module 52 is usable with several geo-referencing systems 26 having variable resolutions. For example, in an implementation, the yield allocation module 52 is usable with a geo-referencing system 26, where each of the geo-referenced regions 70, 72 and 74 has a width corresponding to a collective width of portions 40 In another implementation, the aggregate yield allocation module 52 is usable with a geo-referencing system 26 having a resolution such that each of the geo-referenced regions has a width corresponding to the width of the particular portion 40 that harvested the crops from the associated geo-referenced region.
[0053] In an implementation, the aggregate yield for each measurement interval is equally divided and reallocated among the different geo-referenced regions. For example, in the scheme in Figure 2, one third of the aggregate yield determined for measurement interval 5 is reallocated to each of the geo-referenced regions 70, 72 and 74. As will be described here below, in other implementations, the aggregate yield from each measurement interval it is differently allocated between two or more different geo-referenced regions. In an implementation, the allocation of aggregate income from a particular measurement interval to different geo-referenced regions is f. For example, in an implementation, the allocation of aggregate yield from a particular measurement range is based on the sensed characteristics of the plant for each of the different geo-referenced regions. Such plant characteristics are sensed during the harvest of the plants, such as when the plants are being interacted by the harvester 22, and / or in times preceding the coupling of the plants by the harvester 22. For example, in an implementation, plant characteristics are determined and recorded during cultivation, during application of fertilizer, herbicide or insecticide or by suspended aerial photography / video for subsequent allocation of aggregate yield. In another implementation, the allocation of aggregate yield from a particular measurement range is weighted based on different sizes of different crop removal portions 40.
[0054] As further shown by Figure 2, in the illustrated example, the aggregate yield for each measurement interval for each crop removal portion 40 is consistently allocated back to a geo-referenced region that was traversed during a previous measurement interval . For example, the aggregate yield for each measurement interval for the crop removal portion 40A is consistently allocated back to a geo-referenced region that was traversed by the harvester 22 during a measurement interval preceding the aggregate yield measurement interval by two measurement intervals. The aggregate yield for measurement range 3 is allocated back to the geo-referenced region traversed by the harvester 22 during measurement range 1; the aggregate yield for the measuring range 4 is allocated back to the geo-referenced region traversed by the harvester 22 during the measuring range 2; the aggregate yield for the measuring range 5 is allocated back to the geo-referenced region 70 traversed by the harvester 22 during the measuring range 3; and so on. Likewise, the aggregate yield for each measurement interval for the crop removal portion 40B is consistently allocated back to a geo-referenced region that was traversed by the harvester 22 during a measurement interval preceding the aggregate yield measurement interval by three measurement intervals. The aggregate yield for each measurement interval for the crop removal portion 40C is consistently allocated back to a geo-referenced region that was traversed by the harvester 22 during a measurement interval preceding the aggregate yield measurement interval by four measurement intervals. measurement. This aggregate yield allocation pattern is based on an assumption that the travel time for crops to travel from portions 40 to the aggregate yield sensor 28 does not change. In such an implementation, such travel times associated with the different crop removal portions are determined by the harvester manufacturer 22 through testing and data collection and / or are established and recorded during an initial harvester calibration 22.
[0055] In another implementation, the allocation of aggregate yield from each measurement interval to a geo-referenced region that was traversed during a previous measurement interval varies. For example, in an implementation, the travel times for each of the portions 40 for the aggregate yield sensor 28 are continuously or periodically detected. In one implementation, the combine 22 comprises sensors 80A, 80B, 80C (shown in Figure 1 and collectively referred to as sensors 80) that sense or detect the speed at which crops are transported or the time for crops to travel from each individual portions 40 for the aggregation site 48. For example, in an implementation, times T1, T2 and T3 are determined by sensors 80 comprising cameras that capture a series of images, in which the images are processed to measure displacement times of different harvester sections 22. For example, in an implementation, such sensors 80 determine times T1, T2 and / or T3 by capturing images of a corn cob and tracking motion of the corn cob in a series of time stamp images.
[0056] In an implementation, harvester 22 additionally comprises sensors that sense or detect the speed with which crops move from aggregation site 48 to aggregate yield sensor 28. For example, in an implementation, harvester 22 additionally comprises a sensor 81 (schematically shown) that senses or detects the speed with which the crop moves from the aggregation site 48 to the aggregate yield sensor 28. In such an implementation, the yield allocation module 52 takes into account performance changes in time for crop carriers 42 due to wear and / or due to operational adjustments during crop harvesting. For example, in an implementation, as harvester 22 is harvesting a crop, crop carriers 42 and / or crop carrier 44 experience changes in the rate at which such crop carriers transport the crop. In yet another implementation, travel times are based on control signals establishing the speed of different cultivation carriers 42, 44.
[0057] In one implementation, the transport speed of crop carriers 42 and 44 changes uniformly. In another implementation, the transport speed of cultivation transporters 42 and 44 changes differently, such as when the speed of the cultivation transporter 42C is increased to a greater extent compared to the cultivation transporter 42B. Regardless of their transport speeds for crop conveyors 42, 44 are uniformly or non-uniformly adjusted, the yield allocation module 52 automatically adjusts the aggregate yield allocation based on signals from sensors 80.
[0058] In yet another implementation, the combine 22 additionally comprises a scroll sensor 86 and a pitch sensor 88. The scroll sensor 86 senses and detects the scroll of the combine 22 as it is traversing a growth medium or field. The pitch sensor 88 senses and detects a current pitch of the combine 22 as it is traversing a growth medium or field. The sensed scrolling and pitching of the harvester 22 is recorded in memory 54 and associated with the particular geo-referenced region that was traversed by the harvester 22 when the harvester 22 experienced sensed scrolling and pitching. The scrolling and / or pitching of the harvester 22 impacts the speed with which crops from different portions 40 are transported to the aggregate yield sensor 28. In such an implementation, the yield allocation module 52 automatically adjusts the yield allocation added to geo-referenced regions previously traversed by the combine 22 during previous measurement intervals based on signals from the scroll sensor 86 and / or the pitch sensor 88.
[0059] For example, in an implementation, if the harvester 22 has a forward pitch, angled forward, such as when the harvester 22 is traveling down a slope, transporting crops to an aggregate yield sensor located for back 28 is extended, in which the yield allocation module 52 takes into account the longer time required for the crop to travel to the aggregate yield sensor 28 allocating the aggregate yield from a previous measurement range further back over time for a geo-referenced region traversed during an even earlier measurement interval. If the harvester 22 has a backward pitch, tilted backwards, such as when the harvester 22 is traveling up an incline, the transport of crops to an aggregate yield sensor located backwards 28 is shortened, in which the yield allocation 52 takes into account the shortest time required for cultivation to travel to the aggregate yield sensor 28 by allocating aggregate yield to a geo-referenced region traversed during a more recent measurement interval.
[0060] In an implementation, if signals from the scroll sensor 86 indicate that the harvester 22 has a scroll to the right side of the harvester 22, such as when the harvester 22 is moving across a side of a hill and tilted to the right side of harvester 22, transporting crops from crop removal portions 40 on the right side of harvester 22 may have a longer travel time while transporting crops from crop removal portions 40 on the left side of the combine 22, it can have a shorter travel time due to gravity. In such a circumstance, the yield allocation module 52 automatically adjusts for the side tipping of the combine 22 by allocating the aggregate yield from a later measurement interval to different geo-referenced regions depending on whether the geo-referenced region is harvested by a left side of the harvester 22 or a right side of the harvester 22.
[0061] Likewise, if signals from the scroll sensor 86 indicate that harvester 22 has a scroll to the left side of harvester 22, such as when harvester 22 is moving across a side of a hill and tilted towards the left side of harvester 22, transporting crops from crop removal portions 40 on the left side of harvester 22 may have a longer travel time while transporting crops from crop removal portions 40 on right side of the combine 22 may have a shorter travel time due to gravity. In such a circumstance, the yield allocation module 52 automatically adjusts for the side tipping of the combine 22 by allocating the aggregate yield from a later measurement interval to different geo-referenced regions depending on whether the geo-referenced region was harvested by a left side of the harvester 22 or a right side of the harvester 22.
[0062] Machine control module 54 comprises software, code, circuitry and / or program logic providing instructions for driving processor 32 to adjust operational settings or machine control parameters 24 of harvester 22 based on aggregate yield allocation to different geo-referenced regions. For example, in an implementation, machine control module 54 automatically adjusts operational speeds or transport speeds of cultivation transporters 42 based on allocations of aggregate yield to different geo-referenced regions. In another implementation, machine control module 54 automatically adjusts operational settings for crop removal portions 40 based on aggregate yield allocations to different geo-referenced regions. In yet another implementation, control module 54 automatically adjusts operational settings for a threshing component, such as concave spacing in a combined, based on aggregate yield allocations to different geo-referenced regions.
[0063] The yield mapping module 56 comprises software, code, circuitry and / or program logic providing instructions to direct the processor 32 to map the allocation of aggregate yield to the different geo-referenced regions traversed by the harvester 22. In an implementation , the yield mapping module 56 registers or stores the yield maps for the different geo-referenced regions in data storage 58. Data storage 58 comprises a portion of data storage memory 34. In one implementation, in addition to storing yield maps for different geo-referenced regions, data storage 58 also stores additional data such as aggregate yield for different measurement intervals as well as previously sensed characteristics of the plant that are sensed during the harvesting of such plants or that are sensed in previous times before the plants were coupled by the harvester 22, such as during the application of herbicide, insecticide or fertilizer, cultivation or collection of suspended or aerial cultivation data. As noted above, in different implementations, data storage 58 is conducted by the harvester 22, at a remote location from the harvester 22 and / or is distributed through different rings.
[0064] Figure 1 illustrates a portion of an example of yield map 120 registered in data store 58 by the yield mapping module 56 according to the example of allocation scheme 100 shown in Figure 2. The example of income map yield 120 is illustrated with having three yield map regions 122, 124 and 126. In the illustrated example, each yield map region 122, 124, 126 has a resolution having a width equal to the combined or collective width of portions 40 and one length equal to the distance traveled by the combine 22 during the associated measuring interval. The yield map associate 122 comprises those geo-referenced regions harvested by crop removal portions 40 and traversed by harvester 22 during measurement interval 1. In the illustrated example, the yield for region 122 comprises the portion of aggregate yield from from the measuring range 3, from the measuring range 4 and from the measuring range 5. Also, the yield map region 124 comprises those geo-referenced regions harvested by crop removal portions 40 and traversed by the harvester 22 during measurement interval 2. In the illustrated example, the yield for region 122 comprises the portion of aggregate yield from measurement range 4, from measurement range 5 and from measurement range 6. The region of yield map 122 comprises those geo-referenced regions harvested by crop removal portions 40 and traversed by harvester 22 during intervention measurement range 3. In the illustrated example, the yield for region 122 comprises the portion of the aggregate yield from the measurement range 4, from the measurement range 5 and from the measurement range 6.
[0065] Figure 3 is a flowchart of an example of method 200 for allocating aggregate income to geo-referenced regions. As indicated by block 210, processor 32 receives an aggregate throughput value during a measurement interval. In the implementation example of Figure 1, processor 32 determines the aggregate throughput according to instructions provided by the aggregate throughput module 50 and signals received from the aggregate throughput sensor 28. As indicated by block 212, processor 32 identifies regions geo-referenced crossed by the combine 22 during the measurement interval. Such geo-referenced regions are identified based on signals from the geo-referencing system 26. As indicated by block 216, the yield allocation module 52 allocates portions of aggregate income to regions based on the crop shift times to from different portions of the harvester 22, such as crop removal portions 40. As indicated by block 218, system 20 issues aggregate yield allocations. In the implementation example in Figure 1, this emission is used by machine control module 54 to adjust machine control operational parameter settings 24 and / or through performance mapping module 56 to present and display performance maps, such as such as the performance map 120, on the display 30 and / or storing such maps in the data store 58.
[0066] Figure 4 is a flow chart illustrating an example of method 300 for allocating aggregate crop yield. Method 300 is similar to method 200, except that method 300 applies different allocation weights to different regions. As indicated by block 310, processor 32 receives an aggregate throughput value during a measurement interval. In the implementation example of Figure 1, processor 32 determines the aggregate throughput according to instructions provided by the aggregate throughput module 50 and signals received from the aggregate throughput sensor 28. As indicated by block 312, processor 32 identifies geo regions - cross-referenced by harvester 22 during the measurement interval. Such geo-referenced regions are identified based on signals from the geo-referenced system 26.
[0067] As indicated by block 314, processor 32 receives weightings for the different geo-referenced regions that were crossed before the measurement interval. In an implementation, such weights are based on characteristics of the plants from each of the portions 40 as sensed during the harvest of the plants by the harvester 22. For example, in an implementation, the harvester 22 includes sensors that detect a thickness of each of the stems of the plants being harvested by each of the 40 regions, where the allocation of aggregate yield to each of the 40 regions is weighted based on the sensed thickness of the plants by any of the 40 regions. For example, two geo-referenced regions crossed by harvester 22 during the same measurement interval may receive different aggregate yield allocations due to the stems and one of the geo-referenced regions being thicker or wider than the stems of the other geo-referenced regions, where the greatest stalk thickness is determined to be linked to higher crop yields.
[0068] In another implementation, the two geo-referenced regions traversed by the harvester 22 during the same measurement interval may receive different aggregate yield allocations from a later measurement interval due to other indications reflecting higher yield. For example, in an implementation, harvester 22 detects a crop impact, such as ears of corn, with harvester 22, such as against a harvester extractor plate 22, in which the two geo-referenced regions traversed by harvester 22 during the same measurement range may receive different aggregate yield allocations from a later measurement range due to the detected impacts of cultivation being greater from plants in one geo-referenced region versus another geo-referenced region.
[0069] In another implementation, at least one sensor detects a power characteristic of each of the different components across a crop harvest width of the harvester 22, in which yield allocation weights for different plants in different geographic regions referenced are based on the actual sensed power characteristic and / or differences in the sensed power characteristics of the different components across the crop width. For example, harvester 22 may be harvesting a first geo-referenced region and a second geo-referenced region at the same time across its harvest width. Because the first geo-referenced region provides a higher crop yield than the second geo-referenced region, the power consumed or otherwise used to harvest crops in the first geo-referenced region will in many cases be greater than the power consumed or otherwise employed to harvest crops in the second geo-referenced region. As a result, the power consumed or used by components of the harvester 22 for harvesting crops in the first geo-referenced region is likely to be greater than the power consumed or employed by components of the harvester 22 for harvesting crops in the second geo-referenced region. . Harvester 22 uses power sensors to sense a power characteristic associated with each of the different components through the harvest head and applies different yield allocation weights to different geo-referenced regions based on the sensed and / or effective power characteristic. a relationship between the sensed power characteristic of the different components.
[0070] Examples of components across the harvest width of the combine 22 for which power characteristics must be sensed include, but are not limited to, a pressure roller, a stem cutter and a cutter bar. Examples of sensors used to sensing or sensing the power characteristics that the combine 22 uses to weight yield allocation between different geo-referenced / time stamp regions include, but are not limited to, a voltage sensor, a current sensor , a torque sensor, a hydraulic pressure sensor, a hydraulic flow sensor, a force sensor, a load sensor in the bearing and a rotation sensor. In some implementations, the harvester 22 considers yield allocation among different geo-referenced regions, including time stamp regions, based on sensed power characteristics of more than one type of interaction with the crop across a crop width of the harvester 22. In such implementations, using power characteristics sensed from more than one component of interaction with the crop in each transversal portion of the crop width results in greater weighting accuracy among the different geo-referenced regions / time stamp harvested the different portions of transfer of the crop width.
[0071] In yet another implementation, such weights of income allocation are based on video images or of the plants captured during the harvest. For example, in one implementation, cameras loaded by the harvester 22 capture images of the plants before engaging with the harvester 22, in which such images are analyzed. The results of such analyzes are used to generate and apply income allocation weights. For example, in an implementation, variation and light detection (LIDAR) is used as a basis for estimating yield, in which yield estimates are used to generate yield allocation weights to allocate the aggregate yield detected to different geographic regions. referenced. In still other implementations, these weights are determined based on other characteristics of sensed plants being harvested by the harvester 22.
[0072] In still other implementations, weights of yield allocation are based on historical plant data acquired for the different geo-referenced regions before harvest. Such historical plant data is acquired during field operations at any time from planting to harvest. For example, during field operations such as cultivation or the application of herbicide, insecticide and / or fertilizer, one or more characteristics of the plant are sensed or sensed and stored. Different weightings of income allocation are determined based on such historical data. Certain characteristics of the plant taken at various times are linked to higher yield. For example, taller plants, thicker plants, greener plants can all be linked to higher yields. In such an implementation, if a first geo-referenced region traversed by the harvester 22 during a measurement interval is associated with historical data indicating that the region contained taller plants, thicker plants and / or greener plants during cultivation and / or during the application of herbicide, insecticide, fertilizer, compared to a second geo-referenced region traversed by the harvester 22 during the same measurement interval, the yield allocation module 52 applies a greater weighting of the yield allocation region to the first geo region -referenced compared to the second geo-referenced region. In an implementation, such historical data can additionally or alternatively be acquired through suspended or aerial monitoring of plants within a field prior to harvest.
[0073] As indicated by block 316, the yield allocation module 52 allocates portions of aggregate income to regions based on the crop travel times from different portions of the harvester 22, such as crop removal portions 40, and region weights. Figure 5 schematically illustrates an example of an aggregate income allocation scheme 300 performed by the aggregate income allocation system 20. As with scheme 100 shown in Figure 2, scheme 300 in Figure 5 illustrates the initial removal of cultivation from the growth medium or field for each crop removal portion 40 during each of measurement intervals 1, 2 and 3. The straight arrows in Figure 3 indicate transport of the crop removed to an aggregate yield sensor 28, where the The end of such straight arrows indicates the measurement interval during which the removed crop arrives at the aggregate yield sensor 28 and contributes to a given aggregate yield over an associated measurement range. The curved arrows and allocation in Figure 5 indicate allocation of aggregate yield from each particular measurement interval to geo-referenced regions that were traversed by the combine 22 during previous measurement intervals.
[0074] The aggregate yield allocation scheme 300 is similar to scheme 100 except that allocation scheme 300 additionally illustrates the application of different weightings of region W. As described above with respect to block 314 in Figure 4, in an implementation, such weights W are based on plant characteristics from each of the portions 40 as sensed during the harvest of the plants by the harvester 22 and / or are based on characteristics or data acquired historically with respect to the plants from each of the portions 40 In the illustrated example, the portion of the aggregate yield of measurement range 3 allocated back to geo-referenced region 302 is weighted by the weighting of region W11 which is based on characteristics of plants grown in region 302A. The aggregate yield portion of measurement range 4 allocated back to the geo-referenced region 302B is weighted by the weighting of region W21 which is based on characteristics of plants grown in region 302B. The portion of the aggregate yield of measurement interval 5 allocated back to the geo-referenced region 302C is weighted by weighting the region W31 which is based on the characteristics of the plants grown in the region 302C. As still shown in Figure 5, relationships between regional weights impact the allocation of aggregate income by the income allocation module 52. For example, the aggregate income detected during measurement interval 5 is allocated between regions 306A, 304B and 302C. Region weights impact proportional allocation of aggregate income to geo-referenced regions. In the example schema illustrating 5, the yield allocation module 52 provides the aggregate yield of measurement range 5 among regions 306A, 304B and 302C based on the relationship between the associated region weights W13, W22 and W31. For example, if the weighting of region W22 is greater than the weightings of region W13 or W31, the yield allocation module 52 allocates a larger percentage of a portion of the aggregate yield of measurement interval 5 to region 304B. In an implementation, aggregate income is allocated to different regions based on or in proportion to the relationship between region weights.
[0075] As indicated by block 318, system 20 issues aggregate income allocations. In the implementation example in Figure 1, such emission is used by machine control module 54 to adjust machine control operating parameter settings 24 and / or is used by yield mapping module 56 to present and display yield maps over display 30 and / or storing such maps in data store 58. Figure 6 illustrates an example of yield map 320 resulting from the example of income allocation scheme 300 shown in Figure 5. As shown by Figure 6, yield map 320 comprises yield map regions 322, 324 and 326, with each yield map regions 322, 324, 326 comprising a geo-referenced region from which crops are harvested by crop removal portions 40. Yield map region 322 comprises geo-referenced regions 352A, 352B and 352C (collectively referred to as geo-referenced regions 352) harvested during measurement interval 1 in Fi Figure 5 per crop removal portions 40A, 40B and 40C, respectively. Similarly, yield map region 324 comprises geo-referenced regions 354A, 354B and 354C (collectively referred to as geo-referenced regions 354) harvested during measurement interval 2 in Figure 5 by crop removal portions 40A, 40B and 40C, respectively. Yield map region 322 comprises geo-referenced regions 356A, 356B and 356C (collectively referred to as geo-referenced regions 356) harvested during measurement interval 3 in Figure 5 by crop removal portions 40A, 40B and 40C, respectively. As shown in Figure 6, each of the geo-referenced regions 352, 354 and 356 has an income value based on the aggregate income detected by the aggregate income system 28 during a later measurement interval and as weighted by the region's aggregate income weighting W for the particular region. Yield map 320 has a resolution having a width of each individual crop removal portion 40 and a length corresponding to the distance that the harvester 22 travels during the particular measurement interval.
[0076] Figures 7 and 8 illustrate the aggregate income allocation system 420, an example of implementing an aggregate income allocation system 20. The aggregate income allocation system 420 is similar to the aggregate income system 20 except that the aggregate yield allocation system 420 is illustrated as being specifically used with a combine 422 (in the form of a combination). The aggregate yield allocation system 420 comprises each of the components illustrated and described with respect to Figure 1, some of which are shown and similarly numbered in Figure 7, except that the aggregate yield allocation system 420 specifically includes aggregate yield sensors 732, 734, 736, 738, travel time or transport speed sensors 740, 744, and sensors 748, 750, particular examples of sensors 28, 80, 86 and 88 respectively.
[0077] Combine harvester 422 comprises a chassis 512 that is supported and propelled by ground contact wheels 514. Although harvester 422 is illustrated as being supported and propelled on ground contact wheels 514, in other implementations, the harvester 422 is supported and propelled by complete tracks or semi-tracks. A harvesting platform or head 516 (shown as a corn head) is used to pick up the crop and to transport it to a feeder chamber 518, which serves as an aggregation site for crops from different head portions 516.
[0078] As schematically shown in Figure 7 and shown in more detail in Figure 8, the harvest head 516 comprises a frame 612, row units 614, an auger 615. The frame 612 extends across the physical width of the head harvest 516 and supports row units 614. Row units 614 harvest maize from individual cultivation rows and transport harvested corn to auger 615 for additional transport to feeder chamber 518. Row units 614 are spaced at a side-by-side relationship with each other at a distance compatible with the spacing between adjacent rows of corn to be harvested. As shown in Figure 8, external dividers 616, 618 and central dividers 1620 direct the plants, such as corn stalks, in engagement with each of the row units 614. The central dividers 620 extend between consecutive row units 614. Dividers 616, 618 and 620 cooperate to define longitudinal passages 622 that are centered in relation to the rows to be harvested and a relatively narrow longitudinally extending throat 624 defined by each row unit 614. In some implementations, row units 614 can be adjustable to accommodate other row spacing.
[0079] Figure 9 illustrates an example of row unit 614. Each row unit 614 comprises a frame 626, right and left extractor plates, also known as pallet boards, 628, 630, right and left picker units 632, 634 and pressure rollers 636, schematically shown in Figure 7 below extractor plates 628, 630. Frame 626 supports extractor plates 628, right and left picker units 632, 634 and pressure rollers 636.
[0080] Extractor plates 628, 630 comprise plates having spaced inner edges to define the narrow throat 624. The throat 624 receives corn stalks from an aligned row as the row unit 1214 moves along a row of crops. As the row unit 614 is moved along the row, the stems are pulled down through the throat 624 with the assistance of pressure rollers 636 in such a way that corn cobs loaded by the stalk impact the extractor plates and are separated the stem. The left and right picker units 632, 634 carry the separate ears of corn in a longitudinal direction back to the auger 615. The auger 615 additionally carries the ears of corn separated in directions transverse to the feed chamber 518. Due to the different transverse locations of the different row units 614 (as shown in Figure 8), the ears of corn from the different row units 614 arrive at feeder chamber 518 at different times. In other words, ears of corn from the outer row units or outer head portions 516 are transported, on average, for longer periods of time by auger 615 compared to ears of corn from the most central portions or internal row units, transversely closer to the feed chamber 518.
[0081] As shown in Figure 7, the crop is transported through the feed chamber 518 to a whisk 520. Whisk 520 guides the crop upward through an inlet transition region 522 to a rotating threshing and separating set 524. Although the harvester 422 is described as a rotary combine, in other implementations the harvester 422 can comprise other types of combine (for example combined having a transverse threshing cylinder and straw shakers or combined having a transverse threshing cylinder and rotary separating rotors) or other agricultural harvesting machines including, without limitation, self-propelled forage harvesters, sugar cane harvesters and row harvesters.
[0082] The rotary threshing and separating assembly 524 comprises a rotor housing 526 and a rotor 528 arranged in the rotor housing 526. The harvested crop enters the rotor housing 526 through the inlet transition region 522. The rotating assembly of threshing and sorting 524 threshing and separating the harvested crop. Grain and straw fall through grids at the end of the rotor housing onto a cleaning set 534. Cleaning set 534 removes the straw and leads the cleaned grain to a grain elevator 536 leading up to the grain tank 540. The cleaned grain in the 540 grain tank can be unloaded via an unloading auger 542 on a trailer or truck. The threshed straw separated from the grain is led out of the rotary threshing and separating assembly 524 through an outlet to a dumper 546. The dumper 546 ejects the straw out the rear of the combine 422.
[0083] As schematically shown in Figure 7, in addition to the elements described above of the combine 422, the aggregate yield allocation system 420 further comprises a geo-referencing system 726, an aggregate yield sensor 732, 734, 736, 738 , a display 740, crop carrier sensors 744, 746, a pumping sensor 748, a scroll sensor 750 and a control and yield allocator unit 742. The geo-referencing system 726 comprises a device, including an input location 727, by which different regions of a field are identified, labeled and / or geo-referenced to receive attribution of crop yield characteristics. In an implementation, the 726 geo-reference system specifically identifies a particular region or location in the field that is currently being worked on or traversed by the combine 422. In an implementation, the 726 geo-reference system identifies regions of a field with a resolution such that each individual geo-referenced region has a width substantially equal to a head width 516. In another implementation, the resolution is such that each geo-referenced region has a width of a plurality of row units less than the total width of harvester 422, in which the full width of harvester 422 travels through multiple distinctly identified geo-referenced regions. In yet another implementation, the resolution is such that each geo-referenced region has a width equal to an individual row of plants, with each geo-referenced region having a width corresponding to an individual row of plants or an individual row unit 614. In one implementation, the geo-referencing system resolution 26 identifies geo-referenced regions having a length of a single row of plants, a single plant position across multiple rows. In another implementation, the resolution is such that each geo-referenced region has a length of multiple rows of plants, a series of multiple consecutive plant positions in each row. In one implementation, the 726 geo-reference system comprises an antenna and associated electronics / software as part of a global satellite navigation system (GNSS) or global positioning system (GPS). In other implementations, other devices or other methods and / or technologies are used.
[0084] As schematically shown in Figure 7, the combine 422 comprises multiple aggregate yield sensors: a grain flow sensor 732, an impact plate sensor 734, load sensors 736 and optical sensors 738. The flow sensor Grain 732 comprises a sensor, such as a gamma ray attenuation sensor, positioned along the clean grain elevator 536, which detects or measures the flow of harvested aggregate grain. In other implementations, the 732 grain flow sensor is provided elsewhere.
[0085] The 734 impact plate sensor detects the volume or mass of grain based on the impact of the grain with an impact plate. In one implementation, the impact plate sensor 734 comprises an impact plate mounted so that it deflects in a direction generally parallel to the grain flow direction. Its deflection is dependent on the mass flow of the clean grain. The deflection of the impact plate is measured and thus data on the mass flow of the harvested grain are provided. Such a sensor is described in US patent 5,343,761 (the complete invention of which is incorporated herein by reference) and in the documents cited therein.
[0086] Load sensors 736 comprise one or more load cells underlying portions of grain tank 540. In one implementation, load sensors 736 sense or detect the total weight or mass of tank 540 and the grain retained by tank 540, in which changes in mass indicate aggregate yield. In another implementation, load sensors 736 comprise load cells that detect or measure grain pressure against the walls or surfaces of the tank 540, where changes in pressure indicate aggregate yield. Optical sensors 738 comprise one or more cameras, optical emitter-detector pairs, such as infrared emitter-detector pairs, which detect the amount of grain inside the containment tank 540. In one implementation, such optical sensors 738 detect a level of grain within tank 540, where changes in the grain level indicate aggregate yield. In yet another implementation, such optical sensors 738 additionally or alternatively detect a shape of the grain heap within the grain tank 540, where changes in shape indicate aggregate yield. In one implementation, optical sensors 738 cooperate with load sensors 736 to indicate aggregate throughput. An example of such an implementation is described in US patent application serial number 14/318165 filed on June 27, 2014 by Johnson et al. and entitled GRAIN MASS FLOW ESTIMATION, the complete invention of which is hereby incorporated by reference.
[0087] In the illustrated example, signals from each of the aggregate yield sensors 732, 734, 736, 738 are used to determine an aggregate yield for different measured intervals as the harvester 422 traverses a field during harvest. In one implementation, the different results from the different aggregate yield sensors are specifically prorated or combined in another way. In another implementation, signals from one of the aggregate yield sensors 732, 734, 736, 738 serve as a base measurement, where adjustments are made to the base measurement based on signals from the other aggregate yield sensors . In still other implementations, one or more of such aggregate yield sensors 732, 734, 736, 738 are omitted
[0088] The display 740 comprises a monitor, screen, panel or other device through which information is visibly communicated. In one implementation the display 740 additionally comprises auditory communication capabilities. The display 740 facilitates the presentation of information by identifying the allocation of aggregate income among different geo-referenced regions. In the illustrated example, the display 740 is loaded on board the harvester 422 for viewing by an operator inside the cab 948 of the harvester 422. In another implementation, the display 740 is located remote with respect to the harvester 422, such as when the harvester 422 it is remotely operated or as when remote managers or personnel are analyzing or reviewing aggregate income from different geo-referenced regions in a field.
[0089] Crop conveyor sensors 744, 746 detect the travel time of crops along the head 516 affecting the transport speed of different head portions 516. The conveyor sensors 744 emit signals indicating the speed with which the crop units catch 632, 634 carry the crop, such as ears of corn or other crops, back along each of the row units 614 to the auger 615. In one implementation, each row unit 614 is equipped with an assigned sensor 744 such that different transport speeds of different pickup units 632, 634 along different row units 614 are sensed. The conveyor sensor 746 comprises one or more sensors that emit signals indicating the time for cultivation, ears of corn, to be transversely transported to the feed chamber 518 and / or the auger transport speed 615. In the illustrated example, the time for the crop to be transported from feeder chamber 518 to aggregate sensors 732, 734, 736, 738 is determined based on the time it is verified that the crop being transported reaches feeder chamber 516, based on signals from the sensor 744 and 746 and the rate at which the crop is transported from the feed chamber 518 to the aggregate yield sensors by the various components of the harvester 422 between the feed chamber 518 and the aggregate yield sensors.
[0090] The pitch sensor 748 comprises one or more sensors that emit signals indicating a pitch of the harvester 422. In one implementation, the pitch sensor 748 emits signals indicating a pitch of the head 516, regardless of what the pitch of the rest may be. of harvester 422. The roll sensor 750 comprises one or more sensors that emit signals indicating a harvester 422 roll. In one implementation, roll sensor 750 emits signals indicating a 516 head roll, regardless of what may be a different roll for the rest of harvester 422. In some implementations, one or more of sensors 744, 746, 748 and 750 are omitted, in which predetermined setpoints are used for speeds, pitch and / or roll of the conveyor.
[0091] The control unit and yield allocator 742 comprises a computational component incorporating processor 32 and memory 34 described above. The control unit and performance allocator further comprises the machine controller 24 described above. Although illustrated as being loaded by harvester 422, in other implementations, the control and yield allocator unit 742 is located remotely from harvester 422 or is distributed with portions loaded by harvester 422 and with other remote portions of harvester 422, where communication is facilitated wirelessly using radio frequencies or other wireless technologies.
[0092] In operation, processor 32 of unit 742 receives an aggregate throughput value during a measurement interval. In the implementation example, processor 32 determines aggregate throughput according to instructions provided by aggregate throughput module 50 and signals are received from one or more aggregate throughput sensors 732, 734, 736, 738. Processor 32 from Unit 742 receives signals from location input 727 indicating geo-referenced regions crossed by harvester 422 during the measurement interval. Based on signals from sensors 744 and 746, the yield allocation module 52 of unit 742 determines time differences for crops collected by different portions, different individual row units or groups of row units 614 of head 516 to move for aggregate yield sensors 732, 734, 736, 738. In one implementation, yield allocation module 52 of unit 742 determines time differences for crops collected by different head portions 516 to move to feed chamber 518 and adjusts a predetermined base or travel time from head 615 to tank 540 based on differences.
[0093] Figure 10 schematically illustrates an example of area 749 of a field being harvested by harvester 422 which is eight rows across 36 rows in the area. Figure 10 illustrates a defined example of different travel times for crops that travel from different head portions 516 to a location where crops are sensed for the purpose of determining aggregate yield. The time in which harvested material, such as a corn cob, is harvested arrives in the feed chamber 518 for each plant is shown in each cell. In the illustrated example, the travel time is one second for each row unit 614 above and two seconds per row away from center rows 3 and 4. A delay from the feed chamber to the particular aggregate yield sensor being used , the aggregate yield sensor 734, is 10 seconds. In the example shown in Figure 10, the aggregate yield measured from the measurement range 17.0 to 18.0 seconds is from crops, ears, which enter the feed chamber 518 in the range of 7.0 to 8.0 seconds . These crops, such as ears, are identified in Figure 10 by 1400 shaded subregions. In particular, as indicated by this shading, cultivation rows 1-6, 11-16, 21-26 and 31-36 (indicated in the column, but on the left) they all arrive, at the aggregate yield sensor 734 during the same measurement interval, during the time interval from 17.0 to 18.0 seconds.
[0094] Figure 11 is a diagram illustrating the different times in which crops harvested by the different row unit 614 during the same measurement interval contribute to the aggregate yield detected for subsequent multiple measurement intervals. Figure 11 illustrates harvesting crops by combine 422 having eight row units 614 during 40 measurement intervals, each measurement interval being one second. The time in which crops or plants from a particular row unit 614 arrive at feeder chamber 518 is shown in each cell. As shown in Figure 11, crops harvested by Row 1 at time 17 (indicated in the leftmost time column) arrive at feeder chamber 518 at time 23 (indicated in the cell corresponding to time 17 and Row 1). Crops harvested by Row 2 during the same time 17 arrive at the feeder chamber at time 21, crops harvested by Row 3 during the same time 17 arrive at feeder chamber 518 at time 19 and so on, reflecting the travel time of two seconds per row moving away from center rows 3 and 4. Crops harvested by row 1 at time 18 arrive at feeder chamber 518 at time 24, reflecting the measurement interval of one second.
[0095] In the illustrated example, the delay from the feed chamber to the particular aggregate yield sensor being used, the aggregate yield sensor 734 is 10 seconds. The cells in the aggregate yield monitoring column on the right side of Figure 11 are filled with the time that the crop currently contributed to the aggregate yield for the current measurement interval or time interval previously arrived at the feeder chamber 518. In the example illustrated, the crop arriving at feeder chamber 518 at time 23 (the value contained in the aggregate yield monitoring column on the right side of Figure 11) contributes to the value of aggregate yield at time 33 (as indicated in the rightmost time column left), reflecting the 10 second travel time from feeder chamber 518 to the particular aggregate yield monitor or sensor being used to sense aggregate yield.
[0096] As shown by the shading in Figure 11, the aggregate yield value as detected during time 33 is an aggregate of the crop that arrives at the feed chamber 518 at time 23. As further shown by Figure 11, the crop that arrives at the chamber feeder 518 at time 23 was initially harvested by the different row unit 614 in rows 1-8, at different times due to time differences for the crop to travel to feeder chamber 518. In the example shown, the crop arriving at the feeder chamber 518 at time 23 was harvested by Rows 1-4 at times 17, 19, 21 and 23, respectively, from different geo-referenced regions that were traversed by harvester 22 during different times 17, 19, 21 and 23, respectively . Likewise, crops arriving at feeder chamber 518 at time 23 were harvested by Rows 5-8 at times 23, 21, 19 and 17, respectively, from different geo-referenced regions that were traversed by harvester 22 during different times 23 , 21, 19 and 17, respectively. As indicated by shading, the aggregate yield measured at a particular time, for a particular measurement interval, is the result of crop aggregation harvested from geo-referenced regions in the pattern or shape of a currency, a line or strip in the format of a V or an inverted V, depending on the orientation.
[0097] The yield allocation module 52 of the yield allocation and control unit 742 allocates or inputs the aggregate yield detected during each measurement time or interval back to the previous geo-referenced regions based on the crop shift times from different harvester portions 422, such as from different row unit 614. For example, in the example travel time scheme shown in Figures 9 and 10, unit 742 allocates the measured aggregate yield from the range measurement time 33 back to the geo-referenced regions that were traversed by harvester head 516 422 during measurement intervals or times 17, 19, 21 and 23. Similarly, unit 742 allocates the measured aggregate yield from the interval measurement time 34 back to the geo-referenced regions that were traversed by harvester head 516 422 during measurement intervals or times 18, 20, 21 and 23, allocate the measured aggregate yield for the measurement interval or time 35 back to the geo-referenced regions that were traversed by the header 516 of the combine 422 during the measurement intervals or times 19, 21, 22 and 23, respectively, and so on . As shown by shading in Figure 11, unit 742 of portions or allocates aggregate yield from a particular measurement interval or time to previously traversed geo-referenced regions that are part of a boundary shape.
[0098] In the example of the income allocation scenario illustrated in Figures 9 and 10, the measurement interval is one second. In other implementations, the measurement range is less than a second. In an implementation, the measurement range is between 0.05 seconds and 0.1 seconds to establish a spatial resolution of approximately 61 cm x 61 cm with an overall positioning system error of less than 1.27 cm, facilitating allocations by plant. In circumstances where row spacing is 45.72-96.52 cm for corn and plant spacing within a row is 15.24 cm at a 61 cm by 61 cm spatial resolution contains several plants.
[0099] In other implementations, other measurement intervals are employed. For example, in other implementations, the combine 422 can move across a field at 2 mf, so that it is moving just under 91.5 cm / second. In one implementation, the local position system 726 comprises a GPS receiver that with corrections reports the position with an accuracy of 1.27 cm at a rate of 10 Hz or approximately every 10.16 cm. Maize is often planted with 15.24 cm and 60.96 cm row separation. As a result, GNSS or another positioning system reporting rate and spatial accuracy, combined with known row separation crops, facilitates the allocation of aggregate yield to individual plants.
[00100] In the example of yield allocation scheme shown in Figures 9 and 10, the travel times for cultivation from different row units 614 to the feeder chamber 518 is illustrated as being uniform across the different units 614 transversely located. Rows 1-8, with travel time being uniformly two seconds per row moving away from center rows 3, 4. In other implementations, different harvesters may have different travel times. In addition, such travel times in the same combine can vary at different times and from row to row. For example, harvester 22 and its conveyors, including harvesting unit 62, 634 and auger 615, can operate at different speeds at different times as harvester 22 is traversing a field. In the illustrated example, sensors 744 and 746 emit signals indicating such different speeds at different times, in which the control unit and yield allocator 742 adjusts the allocation or contribution of the aggregated yield to the different geo-referenced regions based on the different speeds or times of crop displacement as indicated by sensors 744, 746.
[00101] In still other times, the harvester 422 may be moving across the side of a hill, resulting in the head 516 having an uneven or uneven roll. The scroll sensor 748 emits signals indicating such an irregular roll. In such a circumstance, crops harvested by row units 614 closest to the top of the hill may have shorter travel times for feeder chamber 518 compared to crops harvested by row units 614 closest to the bottom of the hill due to gravity. The control unit and yield allocator 742 adjusts the allocation or contribution of aggregate income to the different geo-referenced regions based on the different travel times based on the head roll 516 in the time that the particular geo-referenced regions are crossed by the head 516.
[00102] In still other times, the harvester 422 may be moving up or down a hill, resulting in the head 516 suffering from heaving, not being level, but tilted upwards or tilted downwards. The pitch sensor 750 emits signals indicating such pitching. In circumstances where head 516 is tilted going uphill, crops harvested by row units 614 may have shorter travel times for feeder chamber 518 due to gravity assistance. Likewise, in circumstances where head 516 is declined downhill, crops harvested by row units 614 may have longer travel times for feeder chamber 518 due to gravity resistance. The control unit and yield allocator 742 adjusts the allocation or contribution of aggregate yield to the different geo-referenced regions based on the different travel times based on the head pitch 516 in the time that the particular geo-referenced regions are crossed by the head 516. In addition, time for crops to travel from the feed chamber to the aggregate yield sensor can also vary with time and can be adjusted with data from the pitch sensor, roll sensor, or other sensors.
[00103] In an implementation, the travel times for adjustments resulting from changes in pitching or rolling the combine head 516 are additionally based on the type of crop being harvested, crop cleaning being harvested, the moisture content of the crop being harvested from the initial total aggregate income allocations to a particular geo-referenced region. For example, the type of crop being harvested can impact the speed at which harvested crops flow across the head 516 are backwards along the head 516. The amount of foreign material, such as straw, in the grain being harvested and / or the moisture content of the grain being harvested can also impact the speed at which the grain drains. The volume or amount of grain being driven by the head 516 can also impact the speed at which harvested crops flow across the head 516 or backward across the head 516. In one implementation, the control and yield allocator unit 742 adjusts unlike changes in pitching and / or head roll 516 based on the type of crop being harvested, the cleanliness and / or moisture level of the crop being harvested and / or the volume or mass of aggregate yield.
[00104] The control unit and yield allocator 742 issues aggregate yield allocations. In the implementation example, the control unit and yield allocator 742 performs prescriptive adjustment of the combine, adjusting the operational parameter settings of the combine 422 with in aggregate yield allocations. In one implementation, the yield mapping module 56 of unit 742 presents and displays yield maps, such as yield map 120 (shown in Figure 1), on display 730 and / or stores such maps in data storage 58 of the unit 742.
[00105] In an implementation, the control unit and yield allocator 742 additionally bases the aggregate yield allocation on factors or yield allocation weights for different geo-referenced regions and / or the plants grown in such different geo-referenced regions . In one implementation, the control unit and yield allocator 742 identifies delays between crop yield and aggregate yield measurement for each row as harvested by each row unit 614. Such time delays can be variable due to panting and / or collector scroll 518 as well as crop processing elements. Unit 742 additionally defines a data range. Based on time stamp forecasts collected from individual geo-referenced region yield and data with aggregate yield time stamp, the 742 control and yield allocator assigns or allocates aggregate yield for the measurement interval to individual plants and / or individual geo-referenced regions. In an implementation, this forecast data with yield stamp of individual geo-referenced region and aggregate yield data with time stamp or additionally with local stamp, indicating the geo-referenced location based on signals from the location entry 726.
[00106] In one implementation, harvester 422 additionally comprises sensor 770 and / or sensor 772. Sensors 770 and 772 emit signals indicating one or more characteristics of individual plants being harvested or groups of plants as they are being harvested. In such an implementation, the control unit and yield allocator 742 uses such signals to identify or predict differences in yield between different plants and / or different groups of plants being harvested by the different portions, row units 614, of harvester 422. In In one implementation, each row unit 614 includes sensor 770 and / or sensor 772. In another implementation, multiple row units 614, forming different subsets of the entire set of row units 614, each share a sensor 770. Based on the expected yield differences, the control unit and yield allocator 742 adjusts the allocation or contribution of the aggregate yield among the different geo-referenced regions from which plants were harvested by the different units of row 614.
[00107] In an implementation, the 770 sensor comprises a sensor that interacts, engages or contacts the plants as the plants are being harvested, in which this interaction results in signals indicating one or more characteristics of the plants being harvested. In one implementation, sensor 772 comprises a sensor that detects one or more characteristics of the plants being harvested without contacting the plants being harvested. For example, in an implementation, the 772 sensor comprises a camera or LIDAR that emits signals indicating the characteristics of the plants being harvested. In such implementations, the control unit 742 includes software, code or logic programmed to predict yield for different plants or groups of plants based on signals from sensor 770 and / or sensor 772. The forecast yield is used to apply different weights to adjust the allocation of aggregate income among different geo-referenced regions.
[00108] In one implementation, each row unit 614 of head 516 includes a sensor 770 that detects a diameter of each of the plant stems being harvested from each of the geo-referenced regions by the different row units 614 or groups of row units 614. In one implementation, sensor 772 is configured to sense the diameter of individual stems. In such an implementation, unit 742 allocates aggregate yield from a particular measurement range to each of the geo-referenced regions traversed by the different row unit 614 using a weight that is based on the sensed thickness of the plants harvested by integral unit 614 For example, two geo-referenced regions traversed by harvester 422 during the same measurement interval can receive different aggregate yield allocations due to the stems in one of the geo-referenced regions harvested by one of row 614 units being thicker or wider than the stalks in the other geo-referenced regions harvested by other 614 row units, where the greater stalk thickness is determined to be linked to higher cultivation yield.
[00109] In another implementation, the two geo-referenced regions traversed by the harvester 22 during the same measurement interval may receive different aggregate yield allocations from a later measurement interval due to other indications reflecting higher yield. For example, in one implementation, each row unit 614 comprises a sensor 770 that detects a crop impact, such as ears of corn, with the harvester 422, such as an extractor plate 636 from the harvester 422. In one implementation, each sensor 770 can comprise an auditory sensor or an accelerometer to sense the impact of cultivation with the 422 combine. In an implementation, larger or higher impacts producing signals of higher amplitude indicate greater mass and are judged to indicate greater yield. In such an implementation, two geo-referenced regions traversed by harvester 422 during the same measurement interval may receive different aggregate yield allocations from later measurement intervals due to differences in the impacts detected in the crop being greater from plants in one geo-referenced region versus impacts from plants in another geo-referenced region. An example of such a crop impact detection system is described in US patent application serial number. 13771682 filed on February 20, 2013 and entitled CROP SENSING; US patent application serial number. 13771727 filed on February 20, 2013 and entitled PER PLANT CROP SENSING RESOLUTION; US patent application serial number. 13771760 filed on February 20, 2013 and entitled CROP SENSING DISPLAY, whose complete inventions are hereby incorporated by reference.
[00110] In yet another implementation, such weights of income allocation are based on video or images captured from the plants during harvest. For example, in one implementation, the sensor 772 carried by the combine 422 captures images of the plants before engaging with the combine 422, in which these images are analyzed the results of these analyzes used to generate and apply yield allocation weights. For example, in an implementation, variation and light detection (LIDAR) is used as a basis for estimating yield, in which yield estimates are used to generate yield allocation weights to allocate the aggregate yield detected to different geographic regions. referenced. In other implementations, images from other plant portions are used for yield allocation weights using pre-selected or field-calibrated conversion factors or both. For example, in other implementations, yield allocation weights are based on video or captured images of the size of the year on the extraction plate or the size of the plant, in which size of the plant correlates with the mass of the plant which correlates with the mass of grain. In still other implementations, these weights are determined based on other sensed masses of plants being harvested by the harvester 422.
[00111] In another implementation, the 770 sensor detects a power characteristic of each of the different components through a crop cultivation width of the harvester 422, in which yield allocation weights for different plants in different geo-referenced regions are based on the actual sensed power characteristic and / or differences in the sensed power characteristic of the different components across the crop width. For example, harvester 422 may be harvesting a first geo-referenced region with a first row unit 416 or a group of row units 416 and a second geo-referenced region at the same time with a different second row unit 416 or a different second group of row units 416. Because the first geo-referenced region provides a higher crop yield than the second geo-referenced region, the power consumed or otherwise employed to harvest crops in the first geo-referenced region in many instances will be greater than the power consumed or otherwise used to harvest crops in the second geo- referenced region. As a result, the power consumed or employed by components of the first row unit 416 or the first group of row units 416 to harvest crops in the first geo-referenced region is likely to be greater than the power consumed or employed by components of the second row unit 416 or the second group of row units 416 to harvest crops in the second geo-referenced region. Combine harvester 422 uses sensors 770 across the crop width to sense a power characteristic associated with each of the different components through the harvest head and applies different yield allocation weights to different geo-referenced regions based on the effective power characteristic and / or a relationship between the sensed power characteristic of the different components of the different individual row units or groups of row units.
[00112] Examples of components across the harvester crop width 422 for which power characteristics should be sensed include, but are not limited to, a pressure roller, a stem cutter, and a cutter bar. Examples of sensors used to sensing or sensing the power characteristics that the harvester 422 uses to weight yield allocation between different geo-referenced / time stamp regions include, but are not limited to, a voltage sensor, a current sensor , a torque sensor, a hydraulic pressure sensor, a hydraulic flow sensor, a force sensor, a load sensor in the bearing and a rotation sensor. In some implementations, harvester 22 considers allocation of yield among different geo-referenced regions, including time stamp regions, based on sensed power characteristics of more than one type of crop interaction component across a crop width. harvester 422. In such implementations, using power characteristics sensed from more than one crop interaction component in each transverse portion of the crop width results in greater weighting accuracy between the different geo-referenced / seal regions in time harvested by the different portions of the crop width transfer.
[00113] In still other implementations, weights of yield allocation are based on historical plant data acquired for the different geo-referenced regions before harvest. Such historical plant data is acquired during field operations at any time from planting to harvest. For example, during field operations such as cultivation or the application of herbicide, insecticide and / or fertilizer, one or more characteristics of the plant are sensed and stored. Different weightings of income allocation are determined based on such historical data. Certain characteristics of the plant taken at various times are linked to higher yield. For example, taller plants, thicker plants, greener plants can all be linked to higher yields. In such an implementation, if a first geo-referenced region traversed by harvester 422 during a measurement interval is associated with historical data indicating that the region contained taller plants, thicker plants and / or greener plants during cultivation and / or during the application of herbicide, insecticide, fertilizer, compared to a second geo-referenced region traversed by harvester 422 during the same measurement interval, the yield allocation module 52 of the control unit and yield allocator 742 applies a higher weighting of income allocation region to the first geo-referenced region compared to the second geo-referenced region. In an implementation, such historical data can additionally or alternatively be acquired through suspended or aerial monitoring of plants within a field prior to harvest.
[00114] Figure 12 schematically illustrates an example of the 820 crop sensor system. The 820 crop sensor system outputs crop data and field maps with improved resolution. In an exemplary embodiment, the term "resolution" refers to the level of detail with respect to crop data and / or field maps. Resolution for crop data or field maps is determined by the minimum unit for which an attribute is detected or for which an attribute is derived. Generally, the smaller the unit, the higher the resolution. The 820 crop sensor system outputs crop data and field maps using detected or derived attributes and / or identified conditions for individual units or portions of the field having a width less than the used crop harvest width of a combine. For example, even though a combine may have a 12 row crop row, the 820 crop sensor system can output crop data or field maps providing crop attributes such as yield, for less than 12 rows, as in a row-by-row base or even a plant-by-plant base. The 820 crop sensor system can similarly be implemented with respect to non-row crops and non-row harvesters. The increased crop data provided by the 820 crop sensor system facilitates more advanced and sophisticated crop management.
[00115] The 820 crop sensor system comprises an agricultural machine, an example of which is the illustrated harvester 822. The 820 crop sensor system further comprises a display 824, an input 826, a processor 830 and memory 828. The harvester 822 it comprises a mobile machine configured to move through a field or patch of land while harvesting a crop. Combine harvester 822 comprises a head 834, harvester head components 835A-835H (collectively referred to as components 835) and sensors 836A-836H (collectively referred to as sensors 836). In other implementations, the 820 crop sensor system can comprise other types of agricultural machinery.
[00116] The head 834 comprises a mechanism configured to pick and harvest a crop along a thread. The 834 head thread has a used width, Wu, when harvesting crops. In an exemplary embodiment, the width used Wu constitutes that portion of the length or width of thread that is being used to harvest crops at a particular time. Although in most cases, the width used Wu is equal to the physical width of the head thread 834, in some circumstances, the width used Wu may constitute only a portion of the head thread 834, such as along an extreme row, duct water, transport corridor previously harvested and / or similar.
[00117] The 835 harvest components comprise various harvesting mechanisms, such as mechanisms for sectioning or separating the crop from the rest of a plant. Such mechanisms may include knives or blades, extraction plates, rollers, pressure rollers, augers, chains or catching belts and / or the like. In one implementation, the head 834 comprises a corn head for a combination, where the corn head separates ears of corn from the rest of the stalk. In another implementation, the head 834 comprises a head having extraction plates or other mechanisms for sectioning other types of ears from the associated stems. In an implementation, the term "ear" refers to a seed-bearing part of a plant, such as ears of corn, flowers laden with seeds such as sunflowers, pods and the like. In another implementation, the head 834 comprises components for separating cotton from a cotton plant. In another implementation, the head 834 comprises components for separating a plant stem containing sugar or oil from plant leaves. In another implementation, the head 834 may comprise a grain head for a combination, in which the grain along with the is sectioned and subsequently threshed by the combination. In other implementations, the 834 head and the 835 components may have other configurations. For example, although the head 834 is illustrated as being located at a front end of the combine 822 and as being interchangeable with other heads (facilitating the exchange of heads for corn and grain), in other implementations, the head 834 can be supported in other locations by the combine 822 and / or may be a permanent, non-interchangeable component of the combine 822.
[00118] The sensors 836 comprise mechanisms for sensing or sensing one or more power characteristics of its associated harvest components 835. Each of the 836 sensors emit signals based on sensed power characteristics of the one or more associated 835 harvest components. Examples of 836 sensors include, but are not limited to, a voltage sensor, a current sensor, a torque sensor, a hydraulic pressure sensor, a hydraulic flow sensor, a force sensor, a bearing load sensor. and a rotation sensor. Such potency characteristics vary based on the characteristics of the crop plants currently being harvested. For example, plants having thicker stems are often associated with higher yield. Plants with thicker stems are also typically associated with higher potency characteristics. In particular, the components that section the thickest stems, cut the thickest stems and / or transport either the grain or the associated biomass consume greater amounts of power or require greater strength compared to components that interact with plants having thinner stems and lower yields. The greatest amount of power to cut the stem or remove portions of the plant from the stem is in the form of an increase in voltage, an increase in electrical current, an increase in hydraulic pressure flow, and / or an increase in load or force. The 836 sensors detect higher power characteristics and send corresponding signals to the 830 processor.
[00119] Each of the 836 sensors detects one or more values of cultivation attribute for crops harvested by a distinct portion corresponding to the width used Wu. In the illustrated example, each of the 836 sensors detects a power characteristic of components that interact with the plant that indicate a cultivation attribute for plants along an individual row, providing “per row” cultivation attributes. As indicated by a partition 844, the width used Wu is partitioned or divided into 8 equal portions P1-P8, such as row units, where each of the sensors 836A-836H detects power characteristics of components 835 that interact, with crops or plants from portions P1-P8, respectively. In the illustrated example, each portion or each row unit includes a dedicated component 835 and a dedicated sensor 836. In other implementations, the components can be shared between different portions or row units. Likewise, the sensors can be shared between multiple components or multiple row units. In some implementations, instead of establishing cultivation attributes per row, sensors 36 shared between rows alternatively detect power characteristics of components that interact with cultivation or plant to determine cultivation attributes for groups of rows smaller than the total harvest width Wu. The cultivation attributes comprise grain yield and / or biomass yield.
[00120] Although the head 834 is illustrated as including eight sensors, in other implementations, the head 834 may include a greater or lesser number of such sensors along the physical or threaded width of the head 834. For example, a row harvester of cultivation can have more than or less than eight rows, where the combine head can similarly share with more than or less than eight row detection sensors. Although the head 834 is illustrated as being partitioned into equal portions, in other exemplary embodiments, the head 834 is partitioned into uneven portions, in which the sensors detect power characteristics for components that interact with cultivation for the uneven portions. For example, in another implementation, one of the 836 sensors senses or detects power characteristics for crop interacting components that interact with an individual row while another 836 sensor detects power characteristics for interacting crop components that interact. with a plurality of rows.
[00121] As shown in Figure 12, in some implementations, each of the 836 sensors offers a degree of crop detection resolution being configured to sense how power characteristics of crop interaction components change when interacting with each individual plant at the same time. when the combine 822 crosses a field, giving an indication from which an estimate of yield per plant grain or biomass is determined. As shown in Figure 13, in some implementations, 836 sensors can additionally or alternatively sense changes in the power characteristics of crop interaction components when interacting with 848 sets or collections of plants based on time, distance, a number of plants , and / or the like to reduce the amount of data that is processed or stored. Aggregating individual plant data can also improve data usability by eliminating noise in the data.
[00122] The display 824 comprises a device by which information can be visually presented to a harvester operator 282 or to a remotely located monitor / manager / operator of the harvester 822. The display 824 may comprise a monitor or screen that is stationary in nature or that is mobile in nature. In one implementation, the display 824 is loaded by the combine 822 together with the operator. In another implementation, the display 824 comprises a remote stationary monitor from the combine 822. In still other implementations, the display 824 can be of a mobile nature, being provided as part of a tablet computer, smart phone, personal data assistant ( PDA) and / or similar.
[00123] Input 826 comprises one or more devices by which controls and input can be provided to the 830 processor. Examples of input 826 include, but are not limited to, a keyboard, touch panel, touch screen, steering wheel or direction control, a joystick, a microphone with associated speech recognition software and / or similar. Entry 826 makes it easy to enter selections, commands or controls. In implementations where the combine 822 is remotely controlled or remotely driven, entry 826 can facilitate this remote direction.
[00124] Memory 828 comprises a non-transient or non-transient computer readable medium or persistent storage device for storing data for use by processor 830 or generated by processor 30. In an implementation, memory 828 can additionally store instructions in the form of code or software for the 830 processor. Instructions can be loaded into random access memory (RAM) for execution by the 830 processor from a read-only memory (ROM), a mass storage device, or some other storage persistent. In other embodiments, rigid wiring circuitry can be used in place of or in combination with software instructions to implement the described functions. For example, at least memory regions 828 and processor 830 can be embodied as part of one or more application-specific integrated circuits (ASICs). In one implementation, memory 828 is loaded by combine 822. In other implementations, memory 28 can be provided remotely from combine 822.
[00125] In the illustrated example, memory 828 comprises a data storage portion 852, a correlation module 854, condition detection module 856, display module 858 and operations adjustment module 860. The data storage portion 852 contains historical data, such as look-up tables, facilitating data analysis and information sensed by the 836 sensors. The data storage portion 852 is further configured to store the power characteristic values directly detected by the 836 sensors and attribute values of cultivation derived from the power characteristic values directly detected using the 854 correlation module. This stored information can be in various formats such as tables, field maps and / or the like. The data storage portion 852 can additionally store various settings and operator preferences.
[00126] Correlation module 854, condition detection module 856, display module 858 and operations adjustment module 860 comprise programming, software or code stored on a non-transitory medium to direct the operation of the 830 processor. correlation module 854 instructs processor 830 to correlate one or more directly detected power characteristic values, detected by sensors 836 with deducted cultivation attribute values. In other words, correlation module 854 instructs processor 30 and the deduction of cultivation attribute values, such as grain or biomass yield and / or the like, from directly detected power characteristic values. In one implementation, correlation module 854 directs processor 830 to query a look-up table in data storage portion 852 to correlate a power characteristic as sensed by sensors 836 with a grain or grain mass yield value, the deducted cultivation attribute value. In another implementation, correlation module 854 directs processor 830 to solve one or more mathematical algorithms / equations using a sensed power characteristic, and possibly using other additional factors, to deduce a grain or biomass mass yield, a mass that does not that of the grain, of the plant. In other implementations, correlation module 854 directs processor 830 to cultivation attribute values deduced from power characteristic values directly detected in other ways.
[00127] Condition detection module 856 directs processor 830 to identify field and / or crop conditions based on directly detected power characteristic values or deducted cultivation attribute values. Examples of such field / cultivation conditions include, but are not limited to, the absence of plants, a field washing condition, an area of the field having yields that suffer from wheel compaction beyond a predetermined threshold, the existence of a weed patch, loss of yield due to improper application of chemicals, and / or the like. In one implementation, condition detection module 856 directs processor 830 to query a lookup table in the data storage portion 852 to correlate a power characteristic as sensed by sensors 836 and / or a grain mass yield value or deducted grain, the deducted cultivation attribute value, with one of several predefined conditions, examples of which are given above. In another implementation, condition detection module 856 directs processor 830 to solve one or more algorithms and / or mathematical equations using a directly detected power characteristic value and / or a deducted cultivation attribute value and still compare the resulting calculation with one or more predefined thresholds to identify the condition of a field and / or crop. In other implementations, the condition detection module 856 can direct the processor 830 to identify or sense crop and / or field conditions in other ways.
[00128] The display module 858 instructs the processor 830 to generate it and control signals making the display 824 present various information and / or warnings to an operator. For example, the display module 858 can make the 830 processor prompt an operator to select whether or not and how individual power characteristic data should be aggregated, how data should be displayed (graph, chart, field map), what conditions must be identified, how the operator is notified or alerted to these conditions, where such data must be stored and / or similar. The 858 display module further instructs the 830 processor to display data by operator preferences.
[00129] The operations adjustment module 860 comprises code or programming that directs the 830 processor to automatically generate control signals by adjusting operational parameters of the combine 822 based on directly detected power characteristic values or deducted cultivation attribute values. In one implementation, the operations adjustment module 860 generates control signals by independently adjusting operating parameters of distinct head portions 834 over its used width Wu. For example, the operations adjustment module 860 can adjust the operating parameters of a head row unit 834 independently of or differently from one another, head row unit 834 based on directly detected power characteristic values or deducted for the components of interaction with the cultivation of the different row units. For example, the 860 operations adjustment module can automatically, in response to the detected or deducted power characteristic values for interaction components with the cultivation of a particular row unit, generate control signals for an actuator coupled to the extractor plates row unit to adjust the spacing of extractor plates. This adjustment of extractor plates for the particular row unit can be independent and different from the spacing adjustment of other extractor plates for other row units. As a result, improved crop detection resolution provides more refined control over the operation of the 822 combine to better harvest crops.
[00130] Processor 830 comprises one or more processing units configured to execute instructions or wired as part of an integrated circuit for specific application or supplied as code or software stored in memory 828. In the illustrated example, each of the 824 displays, input 826, memory 828 and processor 830 are illustrated as being part of and loaded by the combine 822. In other implementations, one or more of such components may alternatively be located remote from the combine 822 and in communication with the combine 822 wirelessly. In some implementations, some of the aforementioned functions of processor 830 in memory 828 can be shared between multiple processors or processing units and multiple memories / databases, where at least some of the processors and memories / databases can be located remotely with respect to the combine 822.
[00131] Figure 14 is a flow chart illustrating an example of method 900 that can be performed by the 820 system for sensing and estimating grain and / or biomass yield. As indicated by block 910, processor 830 receives detected power characteristic values for each of the multiple crop interaction components through portions of the used width Wu of head 834 of the combine 822. For example, in an implementation where partitioning 844 is employed, the 836A sensor provides the 830 processor with a first power characteristic for crop interaction components that interact with crops along the Pl portion. The 836B sensor provides the 830 processor with a second detected power characteristic value for components of interaction with the cultivation of the P2 portion. The 836C-836H sensors similarly provide the 830 processor with distinct power characteristic values for its interaction components associated with the cultivation of the P3-P8 portions, respectively. In some implementations, the detected power characteristic values may comprise a sensed voltage, a sensed electrical current, the detected torque, a sensed hydraulic pressure, a detected hydraulic flow and / or a sensed load of one or more components interacting with the cultivation.
[00132] As indicated by block 912, processor 830, following instructions provided by correlation module 854, uses the values of power characteristic received (PC) for each of the components of interaction with the cultivation of the different portions to deduce values of cultivation attribute (CAVs) for each portion. Figure 15 is a graph illustrating an example of the relationship of a biomass or grain power and yield characteristic. In the illustrated example, power sensed in the form of watts, in which the wattage of power being used by the component of interaction with the crop for a particular period of time corresponds to biomass and / or grain yield for the particular period of time. In an implementation, an average, median or other statistical power consumption for a crop interaction component over a predetermined period of time is used by the 830 processor to estimate grain and / or biomass yield over the predetermined period of time . In still other implementations, an average, median, or other statistical power consumption for a crop interaction component during a predetermined harvester travel length 822 or such components interacting with a predetermined number of plants is used by the 832 processor to estimate a grain and / or biomass yield for the predetermined length of displacement or predetermined number of cultivation interactions.
[00133] As noted above, the cultivation attribute values comprise an estimate for grain yield and / or an estimate for different grain / biomass mass yield. In such an implementation, the 830 processor deducts an estimated yield for portions that are harvesting a crop. For example, in an implementation where partitioning 844 is employed, processor 830 deducts a first throughput value for the Pl portion, a second throughput value for the P2 portion, a third throughput value for the P3 portion, and so on.
[00134] As indicated by block 914, processor 830 generates control signals, following the instructions contained in the display module 858, to store or display the deduced cultivation characteristics. In one implementation, processor 830 stores cultivation attribute values deducted from the sensed power characteristic in data storage portion 852 of memory 828. In one implementation, processor 830 transmits the cultivation attribute values deducted to a base remote data or local memory via a wide area network, such as a wired or wireless connection. In some implementations, root or base power characteristic data is also stored and / or transmitted. In some implementations, the deducted cultivation attribute values are still displayed on the 824 display. In some implementations, a visible or audible alert or news may be issued by the 824 display in response to the deducted cultivation attribute value for a particular portion satisfying a predefined threshold. For example, if a crop yield deducted for a particular portion P, such as a particular 834 head row unit, falls below a predefined threshold, the operator may receive with an alert or news possibly indicating problems with the unit's operation private row.
[00135] As noted above, as the 820 system determines crop attributes for individual portions of the crop width, such as individual rows or individual plants (or aggregations of plants along a row), the 820 system provides an operator more detailed information having a higher resolution, allowing the operator (or the automatic harvesting machine) to make adjustments to the combine definition on a row-by-row basis to adapt to different conditions that may exist on a row-by-row basis . The operator can also use this information to correlate yield results for individual rows during harvesting with definitions of individual rows from other operations such as planting, tillage, application of fertilizer, insecticide, or herbicide and / or the like. As a result, row-by-row definitions for these other equipment operations such as planting, tillage, fertilizer, insecticide, or herbicide application can subsequently be adjusted based on row-by-row harvest information. For example, strip plows, planters, fertilizer applicators, insecticides, herbicides and / or the like may have given rise to irregular crop development or emergency rates, where row level detection information allows an operator to determine that there is a problem, to identify causes and to identify solutions before the next harvest season.
[00136] Such information can also be used to better calibrate other crop harvest yield devices. For example, yield estimates per row can be used with yield data captured anywhere on the machine, such as a grain yield sensor mounted on the clean grain auger, or outside the machine, such as a weighing scale in a grain storage facility. The combination of these data can be used for purposes such as sensor calibration and post-harvest data processing.
[00137] Figures 16 and 17 illustrate examples of components of interaction with crop 835A of combine 822, for example, right and left pickup units 1032, 1034 and pressure rollers 1036, 1038. Figures 16 and 17 illustrate one of example harvester row unit 1014 822 described above. Row unit 1014 comprises a frame 1026, right and left extractor plates, also known as pallet plates, 1028, 1030, right and left pick units 1032, 1034 and pressure rollers 1036, 1038 (shown in Figure 16). The frame 1026 comprises a U-shaped member supporting the extractor plates 1028, 1030 as well as left and right pickup units 1032, 1034 and pressure rollers 1036, 1038.
[00138] Extractor plates 1028, 1030 comprise plates having internally spaced edges to define a narrow throat 1024. The throat 1224 receives corn stalks from an aligned row as the row unit 1214 moves along a row of crops. As the row unit 1014 is moved along the row, the stems are pulled down through the throat 1024 with the assistance of pressure rollers 1036, 1038 (shown in Figure 16) in such a way that corn cobs carried by the impact of the stem on the extraction plates and are separated from the stem. As noted above, in some implementations, an actuator can be coupled to the extractor plates to automatically adjust the spacing of the extractor plates 1028, 1030 in response to control signals from the 830 processor with detected power characteristic values for the drive unit. private row 1014.
[00139] The right and left harvesting units 1032, 1034 catch the ears of corn and transport these ears back to and from the auger, like auger 615 shown in Figure 8. In the illustrated example, each of the 1032 harvesting units , 1034 comprises driving tree 1040, driving gear 1042, crazy tree 1044, crazy gear 1046, pick chain 1048, and chain drive set 1050. Each of the driving trees 1040 extends from and is driven by a gearbox 1052 to rotate the driving wheel 1042 in rotation. The gearbox 1052 is in turn operably coupled to a torque source 1054 (shown schematically) that provides torque to turn the driving shaft 1040 through the gearbox 1052 each of the driving trees 1040 extends through a corresponding opening 1054 of the frame 1026 (shown in Figure 16). The driving sprockets 1042 cooperate with crazy sprockets 1046 to support and drive the catch chain 1048.
[00140] The 1044 crazy trees are supported in rotation by the 1050 chain drive sets. The 1044 crazy trees in rotation support the 1046 crazy gear wheels. The 1050 chain drive sets adjustably support the 1046 crazy gear wheels for movement between different front and rear positions to adjust the traction of the catch chains 1048. The pressure rollers 1036, 1038 are mounted on a pair of driving shafts 1060 that project forward from the gearbox 1052. The torque source 1054 provides torque to the driving shaft 1060 through gearbox 1052 to rotate the pressure rollers 1036, 1038. As noted above, the pressure rollers 1036, 1038 pull corn stalks down through the throat 1024, between the extractor plates 1028, 1030. As the ears of corn are too large to pass down through the 1024 throat, such ears impact the extraction plates 1028, 1030 and are highlighted or sectioned from the stems to be picked up by the catch chains 1048.
[00141] In the example shown in Figures 16 and 17, the 836A sensor detects a power characteristic associated with interacting components with the 835A crop. In the illustrated example, sensor 836A detects a power characteristic associated with driving pickup units 1032, 1034 and / or pressure rollers 1036, 1038. In an implementation where the torque source 1054 comprises an electric motor, the sensor 836A detects changes in the electric current or voltage of the electric motor, where the changes indicate changes in crop attributes such as stalk thickness, which is used by the 830 processor to estimate grain and / or biomass yield. In an implementation this 1054 torque source comprises a hydraulic or pneumatic motor, sensor 836A detects changes in the hydraulic or pneumatic flow and / or changes in the hydraulic or pneumatic pressure of the hydraulic motor, in which the changes indicate changes in cultivation attributes such as thickness stalk, which is used by the 830 processor to estimate grain and / or biomass yield. As schematically indicated by broken lines, in other implementations, the sensor 836A additionally or alternatively detects physical characteristics of the movement of catch units 1032, 1034 and / or pressure rollers 1036, 1038. For example, in one implementation, the sensor 836A detects changes in force, bearing or torque being imposed on the catch units 1032, 1034 and / or pressure rollers 1036, 1038 as a result of interaction with the crop. The 836A sensor measures or detects force exerted by a crop against a surface, such as pressure rollers 106, 1038, which causes the sensors to deflect or the force being applied to bearings on which pressure rollers 1036, 1038 are mounted. The sensed forces are correlated with biomass yield or grain yield.
[00142] Figure 18 illustrates a portion of combine 1122, another implementation of combine 822. Combine 1122 comprises combine head 1134, torque sources 1135A, 1135B, 1135C (collectively referred to as torque sources 1135), sensors for power characteristic 1136A, 1136B, 1136C (collectively referred to as power characteristic sensors 1136), the geo-referencing system 1140 and the 1142 processor. In the illustrated example, the head 1134 is configured as an accessory for the combine harvester 1122. In other implementations, the head 1134 is attached as part of the combine 1122. The head 1134 comprises a frame 1136 supporting six pickup units or row units 1138 and three stem cutters 1140a, 1140b, and 1140c (collectively referred to as stem cutter 1140 ). Catchment units or row units 1138 pull and pick crops that are transported back to auger 1144. Each row unit 1138 is similar to row unit 1014 described above. In one implementation, each of the 1138 row units uses pressure or pickup rollers to pull crops through a pickup or throat gap 1024, where chain conveyors or pickup units 1032, 1034 carry crops that are plants back to back. the screw auger 1144. Although the combine 1122 is illustrated including six row units 1138, in other implementations, the combine 1122 comprises a larger number of row units or a smaller number of row units.
[00143] Stalk cutters 1140 are supported below 1138 row units to comminute or cut stems or stems remaining on the field and accelerate plant decomposition. Each of the stem cutters 1140 comprises a knife 1146 rotatably supported by the frame 1136 so as to be rotatably driven around a vertical axis 1148. In the illustrated example, each knife 1146 cuts or cuts materials being harvested by two adjacent row units 1138 In other implementations, each 1138 row can have a dedicated rotary knife.
[00144] Torque sources 1135 provide torque to rotate each of the knives 1146 in rotation. In the illustrated example, torque sources 1135 comprise electric drives, such as electric motors. In other implementations, the 1135 torque sources comprise hydraulic motors or other torque sources.
[00145] The 1136 power characteristic sensors detect power characteristics of the 1135 torque sources. In the illustrated example, the 1136A, 1136B, 1136C sensors detect the power characteristics of their associated 1135A, 1135B 1135C torque sources, respectively. The 1136 power characteristic sensors comprise one or more of voltage sensors, current sensors, torque sensors, rotational speed sensors, phase sensors or other appropriate sensors. The 1136 sensors emit signals that are transmitted to the 1142 processor.
[00146] In an implementation, the 1140 geo-referencing system The 1140 geo-referencing system provides the 1142 processor with geo-referenced data with associated sensed power characteristics and deducted or determined cultivation attributes, such as biomass yield or yield of grain, to particular geo-referenced regions, comprises a global satellite navigation system (GNSS). The 1140 geo-referencing system provides data such as global position, speed, orientation, time. The data can only come through satellite navigation signal processing or can additionally or alternatively use data from sensors such as electronic compass, radar speed signals, local positioning system, wheel driven odometer, etc. In another implementation, the 1140 geo-referencing system provides time stamp data linking or associating particular regions of a field with sensed power characteristics and / or cultivation attributes deduced or determined as the combine 1122 traverses a field.
[00147] Processor 1142 comprises one or more processing units that follow the program logic or code contained in a medium readable by a non-transitory computer or 1143 memory in order to use signals received from sensors 1136 and the geo-referencing system 1140 to provide output to display 1160, storage device 1162, harvesting machine controller 1164 and / or yield mapper 1166. In operation, as combine 1122 moves through a crop, the 1140A stalk cutter cuts the 1200a crop. The 1140B stalk cutter cuts the 1200b crop. The 1140C stalk cutter cuts the 1200c crop. The power consumed by each of the 1135 torque sources is measured by 1136 power characteristic sensors. Signals from 1136 sensors are sent to the 1142 processor. This data can be in the form of a power value or like any of the power data. physical parameter sensor in a row, filtered or differently processed shape.
[00148] Processor 1142 processes data from sensors 1136 and optionally from geo-referencing system 1140 and data source 1170. Data source 1170 comprises supplementary data used by processor 1142 to deduce, determine or estimate attributes of cultivation, such as biomass yield or grain yield. In one implementation, data source 1170 includes material power curves such as in Figure 15 to convert sensor values to material values. In one implementation, data from the 1136 sensors are further normalized to contribute to the speed of the combine using data from the 1140 referencing system and the 1170 data source such as plant population, plant variety (eg stalk toughness) .
[00149] Processor 130 generates crop attribute data 150 that is correlated with power consumed by torque sources 1135 that are correlated with crop characteristics 1200a, 1200b, and 1200c being harvested. Stalk cutters processing larger amounts of crop will typically consume more power. In an implementation, the 1142 processor provides output in the form of immediate feedback to a harvest operator operator via the 1160 display. In an implementation, since the 1122 comprises three sections of relative yield, the 1160 display can report and display the distribution as three bars. The bars can represent an absolute amount of materials such as liters of grain or tons of biomass, a deviation from the mean, or similar.
[00150] In another implementation, processor 1142 stores cultivation attribute data 150, in storage device 1162 for further analysis. In one implementation, storage device 1162 is a local persistent storage device carried by the combine 1122. In yet another implementation, storage device 1162 is a remote persistent storage device. Examples of persistent storage devices include, but are not limited to, magnetic disk, non-volatile solid state memory, etc.
[00151] In yet another implementation, processor 1142 transmits crop attribute data 150 to the harvest machine controller 1164. The harvest machine controller 1164 outputs control signals by adjusting the operation of the combine 1122 based on data from cultivation attribute received 150. For example, in an implementation, the 1164 controller, in response to grain and / or biomass yield data, adjusts the operation of the 1135 torque sources to individually adjust the power supplied to the 1146 knives. For example , in one implementation, in response to the reception of signals indicating that the 1138A row unit is harvesting a greater volume of biomass or a greater quantity of grain, the 1164 harvesting machine controller automatically emits control signals increasing the power supplied to the 1146 knife of 1140A stalk cutter. In another implementation, controller 64 additionally or alternatively emits control signals by adjusting speed or torque and is supplied to pickup units 1032, 1034 or pressure rollers 1036, 1038 (shown in Figure 16). Although the harvesting machine controller is schematically illustrated before as being separated from the 1142 processor, in some implementations, the 1142 processor can serve as part of the 1164 controller.
[00152] In yet another implementation, processor 1142 outputs crop attribute data 1150 to yield mapper 1166. Yield mapper 1166 comprises a computing module that combines crop attribute data and geo-referencing data to from the 1140 geo-referencing system to generate maps representing biomass yield and / or grain mass yield across regions of a field. In one implementation, the generated yield map is shown on the display 1160. In one implementation, the generated yield map is additionally or alternatively stored on the storage device 1162. Although the yield mapper 1166 is illustrated as being separate from the 1142 processor, in some implementations, the 1142 processor can serve as part of the 1166 throughput mapper.
[00153] Although harvester 1122 is illustrated as comprising processor 1142, in other implementations, processor 1142 and locator memory 1143 are envisaged as part of a distributed computing environment power and processor 1142 and memory 1143 are alternatively located remotely from the combine harvester 1122, communicating with the harvester 1122 wirelessly over a local area network or a wide area network. Similarly, in an implementation, one or more of the display 1160, the storage device 1162, the harvesting machine controller 1164 and the yield mapper 1166 are located local to the harvester 1122, being loaded by the harvester 1122 as the harvester 1122 crosses a field. Likewise, one or more of the display 1160, the storage device 1162, the harvesting machine controller 1164 and the yield mapper 1166 can also be part of a distributed computing environment, where such devices are located remote from the combine 1122 and where wireless communication with devices occurs over a local area network or a wide area network.
[00154] Figure 19 schematically illustrates the aggregate income allocation system 1320, an example of implementing the aggregate income allocation system 20 described above. As As with aggregate yield allocation system 20, aggregate yield allocation system 1320 allocates aggregate yield to a crop, such as grain or other harvested material such as cane, cotton and other stalks, for different geo-referenced locations or regions . The aggregate yield allocation system 1320 is similar to system 20 except that system 1320 is specifically illustrated as performing method 300 (shown in Figure 4), in which system 1320 applies or uses pre-harvest weight data to allocate the aggregate yield detected among the different geo-referenced locations or regions. Those system components 1320 that correspond to system components 20 are similarly numbered.
[00155] As shown in Figure 19, processor 32 receives or otherwise obtains pre-harvest weight data 1330. Such pre-harvest weight data comprises historical data acquired for the different geo-referenced regions prior to harvest. Such historical data is acquired during field operations at any time from planting to harvest. They can also be purchased before the current growing season. For example, during field operations such as cultivation or the application of herbicide, insecticide and / or fertilizer, one or more characteristics of the plant are sensed or sensed and stored. Different weightings of income allocation are determined based on such historical data. Certain characteristics of the plant taken at various times are linked to a higher yield. For example, taller plants, thicker plants, greener plants can all be linked to higher yields. In such an implementation, if a first geo-referenced region traversed by the harvester 22 during a measurement interval is associated with historical data indicating that the region contained taller plants, thicker plants and / or greener plants during cultivation and / or during the application of herbicide, insecticide, fertilizer, in comparison with a second geo-referenced region traversed by the harvester 22 during the same measurement interval, the yield allocation module 52 applies a greater weighting of region allocation yield to the first geo region -referenced compared to the second geo-referenced region. In an implementation, such historical data can additionally or alternatively be acquired through suspended or aerial monitoring of plants within a field prior to harvest.
[00156] In one implementation, the pre-harvest weighting data is stored in a memory loaded by the harvester 22. In another implementation, the pre-harvest weighting data is remotely stored, where the harvester 22 receives or retrieves the data pre-harvest weighting in a wireless manner, such as over a wireless network.
[00157] As still shown in Figure 19, in the illustrated example, the 1320 system is operable in one of multiple user-selectable modes, in which the operator or user is provided with the opportunity to select which of the pre-harvest weight data is applied to aggregate income allocations. In an operating mode, the 1320 system features selections on the display 30 advising the operator to select more than one type of pre-harvest weight data, in which multiple types of pre-harvest weight data are concurrently used or aggregated in a weighted way to allocate aggregate income to different geo-referenced regions. In an implementation, the 1320 system applies a nominal weight to each of the multiple types of pre-harvest weighting data collected by the operator for use in allocating aggregate yield. In yet another implementation, the 1320 system notifies the operator or otherwise provides the operator with the opportunity to customize the allocation by entering which weights to use for each of the selected types of pre-harvest weight data.
[00158] In still other implementations, the 1320 system uses different types of pre-harvest weighting data for different portions of the field or different geo-referenced regions when considering aggregate yield allocation. In an implementation, the 1320 system, via processor 32, display 30 and an input device, notifies the operator or otherwise provides the operator with the opportunity to identify on a field map which different types of pre-harvest weighting data are available must be applied to different portions or regions of the field. In another implementation, the 1320 system automatically selects which of the different types of pre-harvest weighting data are used to allocate aggregate yield among the different geo-referenced regions. In an implementation, the 1320 system automatically selects which of the different types of pre-harvest weighting data are used based on factors such as a level of confidence in the different types of pre-harvest weighting data for different regions, a level of correlation between yield and one or more yield factors, the level of geographic resolution for different types of pre-harvest weighting data and / or an extent to which different types of pre-harvest weighting data differ statistically across geo regions -referenced.
[00159] For example, historical data may indicate that certain types of pre-harvest weighting data are not very accurate for different particular fields compared to others. In some implementations, the 1320 system can favor particular types of pre-harvest weighting data having higher degrees of geographic resolution compared to others for particular fields. In some implementations, these types of pre-harvest weighting data that do not show significant differences between the different geo-referenced regions to which the aggregate yield is being allocated, are disadvantaged, less likely to be selected by the 1320 system, due to the limited value in estimating income differences between the different geo-referenced regions. In some implementations, differences in values for each type of pre-harvest weight data are compared to a predefined threshold, where those types of pre-harvest weight data have values that are not significantly different across different geo-referenced regions are not used or selected.
[00160] As shown in Figure 19, in the example shown, the 1320 system provides the operator with the opportunity for various types of pre-harvest weighting data for weighting or aggregate yield allocations between the different geo-referenced regions. Examples of different types of pre-harvest weighting data include, but are not limited to, height of crop data 1340, variety data 1342, time slot distributions 1344, weeds, and / or disease data 1346, crop data evapotranspiration 1348, soil moisture data 1350, detected data from plant 1352, cover temperature data 1354 and shadow prevention data 1356.
[00161] As described above, in some implementations, the operator can choose different types of pre-harvest weighting data for different fields or different regions of the field. In other implementations, the 1320 system automatically selects one or more of the different types of pre-harvest weight data for weighting aggregate yield allocations between different geo-referenced regions based on one or more predetermined criteria. In an implementation, the 1320 system automatically selects and uses different types of pre-harvest weight data for different fields or different regions of the field based on such predefined or predetermined criteria.
[00162] In circumstances where multiple different types of pre-harvest weighting data are selected by the operator or automatically chosen by the 1320 system, the multiple types of pre-harvest weighting data can be combined or aggregated to determine the particular weighting of aggregate income to be attributed to each geo-referenced region. In one implementation, each of the multiple types of pre-harvest weighting data is equally weighted, where the expected yield for a geo-referenced region is also based on each of the different yield forecasts from the different types of harvest data. pre-harvest weighting. For example, in an implementation, the different yield estimates or forecasts for a particular geo-referenced region from the multiple different types of pre-harvest weight data for that region are prorated. For example, if 1344 time slot distributions predict a yield of 150 bushels per acre while weed / pest / disease data 1346 predicts a yield of 170 bushels per acre for a particular geo-referenced region, an average yield of 160 bushels per acre ((170 + 150) / 2) will be used to weight aggregate income allocations between the different geo-referenced regions.
[00163] In another implementation, the expected or estimated yields for a geo-referenced region from the different types of pre-harvest weight data are differently weighted. In an implementation, forecasts or yield estimates from these types of pre-harvest weighting data having historically lower levels of accuracy are provided with a lower weight compared to the yield forecasts or estimates from those data types pre-harvest weighting with historically higher levels of accuracy. In an implementation, the different weights applied to the different yield estimates from different types of pre-harvest weight data are uniformly applied across all geo-referenced regions.
[00164] In another implementation, the different weightings applied to the different yield estimates from different types of pre-harvest weighting data are differently or not uniformly applied across all geo-referenced regions. In other words, yield forecasts from different types of pre-harvest weight data are differently weighted against each other depending on the geographic location of the geo-referenced region. For example, in an implementation, yield forecasts for geo-referenced regions of a first portion of a field or in a first location, with soil moisture data 1350, are given greater weight compared to cover temperature data 1354, while yield predictions for geo-referenced regions in a second portion of the field or second location, based on soil moisture data 1350, receive less weight compared to data on cover temperature 1354.
[00165] In an implementation, when combining multiple different types of data to determine an aggregate yield allocation weight, the 1320 system automatically applies and selects the different weights applied to yield estimates from multiple different types of data pre-harvest weighting for geo-referenced regions. For example, in an implementation, the 1320 system queries a query table having different levels of stored or historical accuracy for different types of data or forecasts for different geo-referenced regions. In another implementation, the 1320 system can prompt an operator to launch weighting selections, where the operator chooses the extent to which each of the yield forecasts from each of the multiple different types of pre-harvest weighting data contributes for the final yield estimate for the particular geo-referenced region and the weighted aggregate income allocation weight attributed to the particular geo-referenced region.
[00166] Figure 20 illustrates an example of weighting aggregate income allocations to different geo-referenced regions being harvested using one or more of the pre-harvest weighting data. In the example shown in Figure 20, the aggregate yield for the 1400 shaded subregions of rows 1-8 of Figure 10 is 160 bushels per acre as indicated in column 1502. Yield estimates based on different pre-harvest weight data for each of the sub-regions is shown in column 1504. In some implementations, the pre-harvest weighting data values in column 1504 for each of the sub-regions is the result of an aggregation or combination of multiple yield estimates from different types of pre-harvest weighting data, whether equally weighted or differently weighted. In the example, the average pre-harvest weighting data for the subregions is 175 bushels per acre. The weighted yield data for each region is 1.45 * PHW / 1.57, where PHW i is the yield estimate based on the pre-harvest weighting data for row i. The yield in weighted pre-harvest weight data for each of the eight sub-regions in the example is listed in column 1506. In one implementation, a yield map based on pre-harvest weight data, mapping different pre-harvest weight data harvest and / or corresponding yield estimates for different geo-referenced regions, is stored in polygons or vector, tracking elements or other suitable formats. If a harvested region to be weighted covers more than one polygon / tracking element, a weighting can be determined, for example, using a weighted average, per area, of the covered tracking polygons or region.
[00167] As described above, Figure 19 illustrates a sampling of different types of pre-harvest weighting data. In one example, crop height data 1340 comprises data relevant to crop height at one time or at multiple different times during cultivation, prior to harvest. In an implementation, such data is obtained during activities during the cultivation of the plants, such as during the application of herbicide, insecticide or fertilizer or cultivation. In other implementations, such data is obtained through unmanned or manned aerial vehicles by which crop images are captured during crop cultivation before harvest. Differences in height of crops in the different geo-referenced regions are judged to correlate with differences in yield since the 1320 system determined different weights of income allocation for the different geo-referenced regions.
[00168] In one example, data from varieties 1342 refer to the crop variety. For example, some varieties are judged to have different yield expectations compared to other varieties. In an implementation, such data is obtained from historical records and / or from seed suppliers. Differences in yield expectations for different varieties are used by the 1320 system to determine different weights of yield allocation for different geo-referenced regions where different crop yields are planted.
[00169] In one example, 1344 time slot distributions refer to time slots or time slot distribution from planting the crop in a particular geo-referenced region to the emergence of the crop or some other stage of plant cultivation in the geo-referenced region. In an implementation, such data is obtained during activities during the cultivation of the plants, such as during the application of herbicide, insecticide or fertilizer or cultivation. In other implementations, such data is obtained by recording planting times and unmanned or manned aerial vehicles by which images are captured in time indicating when the crop emerges. Differences in time intervals or time interval distributions are deemed to correlate with different yields, in which the 1320 system uses the expected yield differences to determine and assign different weighting weights to geo-referenced regions.
[00170] In one example, weed, pest and / or disease data 1346 refer to differences in the prevalence or degree of weed, pest and / or disease infestation among the different geo-referenced regions. In an implementation, this data is obtained during activities during plant cultivation, such as during the application of herbicide, insecticide or fertilizer or cultivation. In other implementations, such data is obtained through unmanned or manned aerial vehicles by which crop images are captured during crop cultivation before harvest. In an implementation, the 1320 system will assign a geo-referenced region having a high degree of weed, pest or disease infestation with a lower yield allocation weight compared to the yield allocation weights assigned by the 1320 system to other geo-referenced regions having a lesser degree of weed or pest infestation or disease.
[00171] In one example, evapotranspiration data 1348 refers to the timing and rate at which moisture or water is transferred from each individual geo-referenced region to the air or atmosphere through evaporation from the soil and by transpiration at from crops or plants. Such data can be obtained through the use of evapotranspiration sensors positioned in the place or field or through the use of evapotranspiration sensors transported by air or remote by satellite. In an implementation, the 1320 system correlates differences in evapotranspiration between different geo-referenced regions with different predicted yields according to historical data or correlation formulas, in which the 1320 system still assigns different allocation weights to different geo-referenced regions with based on the different expected yields.
[00172] In one example, soil moisture data 1350 refers to differences in the level of soil moisture in the different geo-referenced regions at a particular measurement time or at multiple different measurement times during crop cultivation. Such data can be obtained through various soil moisture sensors, whether local field position sensors or aerial or satellite sensors. Based on differences in soil moisture or the amount of soil moisture in stages of particular cultivation of crops in different geo-referenced regions, the 1320 system determines an expected yield or a predicted yield ratio between the different geo-referenced regions. The 1320 system assigns different allocations of aggregate yield to different geo-referenced regions based on the different expected yields or expected yield ratios based on certain differences in soil moisture.
[00173] In one example, the 1320 system uses data detected on the 1352 plant as a basis for different allocations of aggregate income attributed to different geo-referenced regions. For example, in an implementation, sensors on the plant are provided on each plant or a sample of plants within each geo-referenced region. Data is obtained and stored from these sensors on the plant. In one implementation, the 1320 system predicts differences in yield or yield ratios between different geo-referenced regions based on different sensor data about the plant retrieved from the different geo-referenced regions. In another implementation, such differences in expected yield or yield ratios are determined by an external provider, where differences in expected yield or yield ratios are provided to the 1320 system. Examples of these sensors on the plant include, but are not limited to to sensors that measure light intensity, soil moisture, fertilizer conditions, soil consistency, atmospheric moisture and temperature, stem diameter, leaf size, leaf moisture, turgidity, plant hormone levels, and plant color.
[00174] In one example, cover temperature data 1354 refers to the sensed temperature of the crop cover across the different geo-referenced regions. The cover temperature is a direct measure of the energy released by the plant and / or the plant's water stress. In an implementation, such a cover temperature can be sensed or monitored using local, suspended or overhead infrared temperature sensors or infrared thermometry systems. This cover temperature provides information on water status, use of water use and how a plant is working metabolically. Monitoring a cover temperature facilitates the determination of heat unit accumulation. In an implementation, such cover temperature or cover temperature variations across different geo-referenced regions can indicate or predict different crop yields for different geo-referenced regions. In an implementation, the cover temperature can be estimated as a function of the ambient air temperature. Ambient air temperature can be measured near geo-referenced regions or based on measurements at one or more remote locations. In an implementation, based on the expected crop yield differences due to the sensed and recorded differences in cover temperatures taken at one or more crop cultivation times or stages, prior to harvest, the 1320 system assigns different yield allocation weights aggregated to the different geo-referenced regions.
[00175] In one example, shadow prevention data 1356 refers to data reflecting differences in expected yield for different geo-referenced regions due to the shadow prevention phenomenon. Evidence suggests that crop yield is sometimes reduced by the presence of weeds in the most premature vegetative states through a shade-preventing effect. Plants detect the green light reflected by nearby plants and respond to avoid shade. Such plants respond by directing more resources to plant growth as well as at the expense of roots growing deeply. Shallow roots can impact subsequent yield by limiting the plant's ability to access moisture and nutrients more deeply into the soil.
[00176] In an implementation, shadow prevention data 1356 is acquired through periodic collection, such as daily collection, of images of the field after planting. These images of the field are processed to generate maps of vegetation, geo-referenced ortho-rectified showing emerged cultivation and other vegetation. Plants that are cultivated are identified in images based on a map as planted, the probability of being in a cultivation row and thus a cultivated plant, spectral reflectance of the leaf, the shape of the leaf and other technologies currently developed or future. Other plants in such images are assumed to be weeds or they can be explicitly identified as a weed or specific weed species through spectral reflectance, leaf shape and other technologies currently developed or developed in the future. In some implementations, data from LIDAR or a stereo camera is used to measure the height of a crop and weed. In some implementations, height is also used as a factor in estimating impact of crop yield from weeds along with weed species, and crop variety, in which different crop varieties may have different susceptibilities to the prevention effect of shadow.
[00177] Figure 21 schematically illustrates an example of a 1600 aggregate income allocation weighting by shadow prevention system to use 1356 shadow prevention data to differently weight aggregate income allocation among different geo-referenced regions based on differences in shadow prevention data from the different geo-referenced regions. In an implementation, the 1600 system is incorporated or integrated into the 1320 system described above. System 1600 comprises geo-referencing system 26, aggregate yield sensor 28, data sources 1604, processor 32, described above, instructions 1606, stored yield map data 1608 and display 1610.
[00178] The geo-referencing system 26, the aggregate yield sensor 28 and the processor 32 are each described above. Data sources 1604 comprise databases from which processor 32, following instructions 1606, retrieves data to determine weight loss throughput for shadow prevention. In one implementation, data sources 1604 are local with respect to processor 32. In one implementation, data sources 1604 and processor 32 are local with respect to harvester 22, being loaded by harvester 22. In another implementation, the data sources 1604 and processor 32 are remote with respect to harvester 22, communicating wirelessly with harvester 22. In still other implementations, processor 32 is local with respect to harvester 22, being loaded by harvester 22 , while data sources 1604 are remote with respect to the harvester 22, communicating with the harvester 22 wirelessly. In some implementations, processor 32 and portions of data sources 1604 are distributed across multiple locations and / or remote locations.
[00179] Data sources 1604 comprise crop variety data 1620, vegetation map 1624 and map as planted 1626. Crop variety data 1620 comprise an information database with respect to the particular variety or varieties of plant of cultivation in regions geo-referenced by the system of geo-referencing 26. Such data help to identify or distinguish crops from foreign vegetation. In one implementation, crop variety data 1620 provides historical data with respect to anticipated yield losses for different amounts of vegetation in the vicinity of a crop plant for a particular crop variety. This information can be provided in the form of an equation, algorithm, model or other suitable form.
[00180] The vegetation map 1624 comprises a map or image of the geo-referenced regions and the vegetation in these geo-referenced regions, including both crop plants and foreign vegetation, such as weeds. In an implementation, the vegetation map is generated based on images captured at one or more times or at one or more different stages of cultivation during the growing season before harvest. The vegetation map is stored for subsequent aggregate yield allocations during crop harvest. In an implementation, such images are captured by aerial or other suspended image capture devices such as unmanned aerial vehicles, manned aircraft, balloon, satellite or other aerial vehicles. In other implementations, the 1624 vegetation map is obtained from a camera mounted on a manned vehicle on the ground or through cultivation manual cultivation scanning. In an implementation, the vegetation map is ortho-rectified and geo-referenced.
[00181] The map as planted 1624 comprises a map identifying planting characteristics of the different geo-referenced regions. In one implementation, the map as planted 1624 includes data indicating the varieties of crop plants planted as well as the spacing of these crop plants. In some implementations, the map as planted 1624 also includes additional historical data such as levels of fertilizer, insecticide or herbicide applied to each of the different geo-referenced regions. In some implementations, the map as planted 1624 is omitted.
[00182] Instructions 1606 comprise instructions or programmed logic contained on a computer-readable medium-non-transitory or memory. Instructions 1606 comprise the shadow loss yield module 1630 and the aggregate yield allocation module 1632. The shadow loss yield module 1630 directs processor 32 in estimating loss losses through loss prevention for each of the different geo-referenced regions. The aggregate yield allocation module 1632 directs processor 32 to allocate a sensed aggregate yield to the different geo-referenced regions, in which the allocation of the detected aggregate yield is based on a different travel time for the crops to travel until the aggregate yield 28 and different weightings of income loss due to shadow prevention in the different geo-referenced regions, as per method 300 described above.
[00183] In an implementation, modules 1630 and 1632 cooperate to drive processor 32 to perform the exemplificative method 1700 outlined in Figure 21. As indicated by block 1720, model 1630 drives processor 32 to access data storage 1600 and obtain a 1624 vegetation map of the geo-referenced regions being harvested. As indicated by block 1722, module 1630 further directs processor 32 to acquire crop variety data by accessing crop variety data 1620 from data sources 1600. As indicated by block 1724, module 1630 still directs processor 32 to identify cultivation plants on the recovered vegetation map. In other words, module 1630 directs processor 32 to distinguish crop plants from non-crop plants or foreign plants, such as weeds. In one implementation, processor 32 uses crop variety data to identify crop plants on the recovered vegetation map. In one implementation, processor 32 uses the map as planted to identify crop plants from non-crop plants. For example, since processor 32 identifies a first crop plant, processor 32 uses the spacing stored between planting the crop plants to assist in the positive identification of additional crop plants that will have the same planted spacing with respect to first cultivation plant.
[00184] In some implementations, the identification of cultivation plants in the vegetation map is still achieved based on the probability of being in a cultivation row and thus being a cultivation plant, in the spectral reflectance of the leaf, leaf shape and others criteria. Plants not identified as crop plants identified as weeds by processor 32. In yet other implementations, processor 32 additionally or alternatively positively identifies weeds or weed species through spectral leaf reflectance, leaf shape and other criteria, in that plants not identified as weeds are linked as more likely to be crop plants.
[00185] As indicated by block 1728, module 1630 uses the identification of cultivation plants in the vegetation map to calculate weight loss yields through shadow prevention. In one implementation, processor 32 uses a detailed 3-D ray tracer light model from the sun, for weeds, for nearby crop plants to calculate how much green light is reflected from the weed leaves to the cultivation plant. Weed leaf size and position are considered in such an approach. In another implementation, processor 32 determines the amount of green light based on a sum of a function applied to all weed leaves in a region of interest, where the function considers the area of each weed, the location of the centroid, and the distance from the weed centroid to the centroid of the crop plant that receives the light. In one implementation, the area of each weed, the location of the centroid, and the distance from the weed centroid to the cultivating plant centroid that receives its light are directly based on a 2-D analysis of the vegetation map.
[00186] Figure 23 illustrates an example of calculating weight loss through shadow prevention by processor 32 following the instructions provided by module 1630. As shown in Figure 23, in the illustrated example, the PI 1802 cultivation plant has three weeds 1804, 1806 and 1808 in close proximity. Weeds 1804, 1806 and 1808 have C1, C2 and C3 centroids; areas al, a2 and a3; and distances dl 1, dl2 and dl3 from each respective centroid to the plant centroid 1802, respectively. In the illustrated example, module 1630 directs processor 32 to determine weight loss yields through shadow prevention as a function of the area of each weed, and the distance from each weed centroid to a 1802 plant centroid. example illustrated, the weight loss yields for shade prevention for PI 1802 plant (YLWi) is YLW = K * SUM n = 1 to3 of (an / (din) 2), where K is a constant for the crop variety which is retrieved from crop variety data. K represents a susceptibility and magnitude of shadow prevention and gives cultivation the weighting of loss of income by prevention (YLW) the appropriate units for further calculations.
[00187] Figure 24 illustrates another example of a method by which processes 32, under the direction of module 1630, determine weightings of loss of yield due to shadow prevention. As shown in Figure 24, in the illustrated example, numerical computation is reduced using a grid centered on each P 1850 crop plant. For each element of grid 1852, a weight reduction of Wrc yield is calculated as a percentage of the vegetative pixels PVPrc in each element that is multiplied by a plant factor PRrc that is part of the crop variety data. The plant factor takes into account the shadow prevention response for variety cultivation at the distance of the respective grid element from the 1850 plant. The rc subscripts are for the row and column of each grid element, respectively. Under this approach, the weight reduction of Wrc yield for each grid element is calculated according to the formula Wrc = PVPrc * PFrc. PVPrc, like k, for selecting weeds and cultivation varieties, is determined empirically through, for example, laboratory experimentation, in which cultivation plants are planted in a normal pattern with weeds growing in one or more cells of the grid. The shade-preventing effect invoked by weeds for nearby crop plants is compared to the yield of control plants that do not receive green light reflected by the weed leaves.
[00188] Recognizing that the reduction in yield from roots in the shade resulting from the shade prevention response can be more severe in dry years than in wet years as well as more severe in high areas than in low areas, other equations or factors can be used to calculate Wrc. For example, in an implementation, the following more detailed formula is used by processor 32 to calculate weight loss weights due to shadow prevention: Wrc = PVPrc * PFrc * LSPrc * R, where LSPrc is a landscape position factor or topological and R is a rain factor for the season (shown in the particular example as being a constant for the field). In one implementation, the weight loss yields for shade prevention for each individual plant are then added for all plants in a particular geo-referenced region to determine the weight loss yield for shade prevention for the geo-referenced region private.
[00189] In an implementation using the approach described with respect to Figure 24, the weight loss loss due to shadow prevention for each plant is a sum of the weights for all grid elements. In another implementation, as shown in Figure 25, under the approach described with respect to Figure 24, the weight loss loss through shadow prevention for each plant is calculated using more complex functions such as a sigmoid function. In one implementation, the weight loss yields for shade prevention for each individual plant are then added for all plants in a particular geo-referenced region to determine the weight loss yield for shade prevention for the geo-referenced region private.
[00190] In some implementations, the map as planted is not available or image processing is not performed to identify local crop plants on the vegetation map. In such circumstances, this lack of effective plant location is manipulated using a pseudo plant location with map rows and seed intervals appropriate for the plant population in row spacing.
[00191] As indicated by block 1730 in Figure 21, aggregate income allocation model 1632 (shown in Figure 21) uses the loss of income determinations by shadow prevention for each of the geo-referenced regions to weight differently the allocations of aggregate income among the different geo-referenced regions. The 1632 aggregate yield allocation model allocates each of the geo-referenced regions and aggregate yield portion allocation based on different travel time for crops for the aggregate yield sensor and differences in yield loss weights due to shadow prevention for each of the different geo-referenced regions. As noted above, in some implementations, additional income is used in conjunction with weight loss data from shadow prevention to weight aggregate income allocations differently across different geo-referenced regions. For example, in an implementation, the aggregate yield allocation module 1632 can direct processor 32 to weight aggregate yield allocations among the different geo-referenced regions based on a combination of shadow prevention loss data and any of the other types of pre-harvest weighting data, such as those shown in Figure 19.
[00192] As indicated by block 1732 in Figure 21, the aggregate income allocation module 1632 issues the aggregate income portion allocations. As indicated by arrow 1640 in Figure 21, module 1632 directs processor 32 to issue such aggregate yield portion allocations to a database that stores yield map 1608. As indicated by arrow 1642 in Figure 21, in an implementation , module 1632 directs processor 32 to display the performance map on display 1610. In one implementation, display 1610 is connected to processor 32 via a wired or wireless connection. In some implementations, the 1610 display is wearable, portable, vehicle-mounted, part of a tablet or part of a personal computer or any other appropriate display device. In some implementations, the 1610 display comprises a surface on which an image is projected.
[00193] In some implementations, to different yields among the different geo-referenced regions, resulting from the different allocations of aggregate income, a color is assigned that is presented on the display 1610, in which each assigned color indicates the number of liters or in which income range within which effective yield falls. In other implementations, the different yields of the different geo-referenced regions are represented with different patterns, such as blanks, dashes and the like. In other implementations, different yields are represented in different gray scales on the 1610 display. In some implementations, different yields are given a proportional height above a plane in a 3-D view of the field being presented on the 1610 display. some implementations, two or more tactile representations are employed, such as color and height above a plane or color and pattern. In some implementations, color is used to represent aggregate yield for an area with intensity used to illustrate relative yield at a higher resolution. In still other implementations, processor 32 uses existing market prices to calculate and display an illustration of all or a portion of a field with units of "lost dollars" from shadow prevention. For example, using this yield loss data for shade prevention, processor 32 determines such loss based on the number of bushels per acre of its loss multiplied by the sale price of the crop per liter per square meter in the region.
[00194] Although methods for determining and applying shadow loss yield data are described to differently weight aggregate yield allocations between different geo-referenced regions that are based on different travel times for crops to an aggregate yield sensor , in other implementations, the same methods can, in other implementations, be used to highlight an existing or preliminary performance map. Figure 26 illustrates an example of a 1900 method that produces and displays an enhanced yield map using calculated weight loss through shadow prevention. In the illustrated example, method 1900 is performed by the system 1600 shown and described with respect to Figure 21 without the use or provision of the geo-referencing system 26, the aggregate yield sensor 28 or the aggregate yield allocation module 1632. These Method 1900 steps that correspond to Method 1700 steps are numbered similarly.
[00195] As indicated by block 1904, module 1630 directs processor 32 to obtain a preliminary yield map. The preliminary yield map comprises a map of multiple geo-referenced regions of a field to which preliminary or initial crop yield values have been assigned. In one implementation, the preliminary yield map includes resolution units having a width of a crop harvester head and a length that is a distance or time range of travel by the harvester. In implementations, the vegetation map 1624 is at a finer resolution, in which the enhanced yield map results from the modification of the interval yield map by the 1900 method and the resolution is equal to or lies between the resolution of the yield map preliminary and the vegetation map 1624.
[00196] As further illustrated by blocks 1722, 1724, 1726 and 1728 in Figure 26, method 1900 performs the same corresponding steps as described above with respect to method 1700. In particular, module 1630 directs processor 32 to obtain a map of vegetation, obtain data from 1620 crop variety, identify crop plants on the vegetation map and calculate weight loss weights due to shade prevention.
[00197] As indicated by block 1930, processor 32, following the instructions provided by module 1630, applies certain weight loss through shadow prevention weights to individual plants in each preliminary yield map of the resolution unit. In an implementation, the preliminary yield map comprises yield data representing an average yield in a defined area (the resolution of the preliminary yield map), where the area is at a coarser resolution than the weighted loss of income by shadow prevention, all Wtot. As a result, the average yield in each defined area is spatially redistributed in the area to provide higher resolution yield estimates within the defined area. As indicated by block 1932, the highlighted yield map is then stored in database 1608 and / or displayed on display 1610 as described above.
[00198] Figure 27 schematically illustrates the 2020 crop sensor system, an example of implementing the 1320 system. The 2020 system is similar to the 820 system described above except that the 2020 system additionally comprises 2036A-2036H crop sensors (collectively referred to as cultivation sensors 2036). In addition, the 2020 system is specifically illustrated as comprising the geo-referencing system 26, the aggregate yield sensor (s) 28, the aggregate yield module 50, the yield allocation module 52, the yield module yield mapping 56 and pitch / roll sensors 86, 88 (each of which is described above with respect to system 20) that perform method 300 (described above in Figure 4), in which the region weightings in block 314 of Figure 4 comprise weightings based on different yield estimates for the different geo-referenced regions from the following examples of yield estimation mechanisms: (1) data and / or signals from 836 sensors that are based on power for each one of the combine head portions 834; (2) data and / or signals from the 2036 crop sensors that are based on directly detected attributes of the crops in each of the geo-referenced regions as these crops are harvested; and / or (3) one or more of pre-harvest weighting data 1336, such as crop height data 1340, variety data 1342, time distribution data interval 1344, weed / pest / disease data 1346, evapotranspiration data 1348, soil moisture data 1350, plant detection data 1352, cover temperature data 1354 and / or shadow prevention data 1356. As described above, in some implementations, multiple weighting bases or weighting mechanisms income estimation are combined in equally or differently weighted ways to determine the different allocation weights.
[00199] As described in more detail above, in some implementations, different weighting bases or income estimation bases are used to differently weight aggregate income allocations based on the particular region or geo-referenced regions. For example, in an implementation, a first or a first set of the income estimation mechanisms described above are used to weight aggregate income allocations for a first geo-referenced region as a different second or different set of income estimation mechanisms. income described above are used to weight aggregate income allocations for a second geo-referenced region.
[00200] Furthermore, as described in more detail above, in some implementations, even though the same yield estimation mechanisms are used across different geo-referenced regions, the same yield estimation mechanisms can have different relative weights when combined with based on the geo-referenced region to which the income estimation mechanisms are being applied. For example, in an implementation, even if two geo-referenced regions have aggregate income allocations that are weighted based on the same first and second income estimation mechanisms, the 2020 system applies a greater weight relative to the first income estimation mechanism. income on one of the geo-referenced regions while applying a greater weight relative to the second income estimation mechanism over the other of the geo-referenced regions.
[00201] Although the 2020 system is illustrated as providing an operator with the ability to choose from different modes of operation, where the operator selects any or any combination of the performance estimation mechanisms described or where the 2020 system chooses automatically from among the income estimation mechanisms described above, in other implementations, the 2020 system uses less than each of the described income estimation mechanisms described above. In some implementations, the 2020 system may use additional or alternative yield estimation mechanisms when considering differently the aggregate yield allocation among the different geo-referenced regions based on crop displacement times for the aggregate yield sensor.
[00202] The 2036 harvest sensors comprise sensors that directly detect one or more attributes of the crops as the crops are being harvested by the 820 harvester. The 2036 cultivation sensors emit signals indicating one or more characteristics of individual plants being harvested or groups of plants as they are harvested. In such an implementation, yield allocation module 52 uses such signals to identify or predict yield differences between different plants and / or different groups of plants being harvested by the different portions, row units, of the combine 820. In one implementation, each row unit includes a designate of the 2036 sensors. In another implementation, multiple row units, forming different subsets of the entire set of row units share a 2036 sensor. Based on the expected performance differences, the model of yield allocation 52 adjusts the allocation or contribution of aggregate income among the different geo-referenced regions from which plants were harvested by the different row units.
[00203] In one implementation, each of the 2036 sensors detects a diameter of each of the plant stems being harvested from each of the geo-referenced regions by the different row units or groups of row units. In one implementation, each of the 2036 sensors is configured to sense the diameter of individual stems. In such an implementation, the yield model 52 allocation allocates aggregate yield from a particular measurement range to each of the geo-referenced regions traversed by the different row unit using a weight that is based on the determined plant thickness harvested by each unit row. For example, two geo-referenced regions traversed by the 820 combine during the same measurement interval may receive different aggregate yield allocations due to the stems in one of the geo-referenced regions harvested by one of the row units being thicker or wider than the stems in the other of the geo-referenced regions harvested by the other row unit, where the greater thickness of the stalk is determined to be linked to greater cultivation yield.
[00204] In one implementation, each of the 2036 cultivars comprises a sensor that interacts, engages or contacts the plants as the plants are being harvested, in which such interaction results in signals indicating one or more characteristics of the plants being harvested. For example, in one implementation, each of the 2036 sensors comprises a sensor that senses a crop impact, such as ears of corn, with the 820 harvester, such an 820 harvester extraction plate. In one implementation, each of the 2036 sensors it can comprise an auditory sensor or an accelerometer to detect the impact of cultivation with the 820 harvester. In an implementation, higher or greater impacts producing signals of higher amplitude indicate greater mass and are judged to indicate greater yield. In such an implementation, two geo-referenced regions traversed by the 820 combine during the same measurement interval can receive different aggregate yield allocations from later measurement intervals due to differences in the sensed impacts of the crop being greater from plants in one geo-referenced region versus impacts from plants in another geo-referenced region. An example of such a crop impact detection system is described in US patent application serial number. 13771682 filed on February 20, 2013 and entitled CROP SENSING; US patent application serial number. 13771727 filed on February 20, 2013 and entitled PER PLANT CROP SENSING RESOLUTION; US patent application serial number. 13771760 filed on February 20, 2013 and entitled CROP SENSING DISPLAY, whose complete inventions are hereby incorporated by reference.
[00205] In one implementation, each of the 2036 cultivation sensors comprises a sensor that detects one or more characteristics of the plants being harvested without contacting the plants being harvested. In such an implementation, these yield allocation weights are based on video or images captured from the plants during harvest. For example, in an implementation, each of the 2036 sensors comprises a camera that captures images of the plants before engaging with the combine 820, in which such images are analyzed and the results of these analyzes are used to generate and apply yield allocation weights . In one implementation, each of the 2036 crop sensors comprises a camera or LIDAR that emits signals indicating the characteristics of the plants being harvested. In such implementations, yield allocation module 52 includes software, code or logic programmed to predict yield for different plants or groups of plants based on signals from the 2036 sensors. In some implementations, images of other plant portions are used for yield allocation weights using pre-selected or field calibrated conversion factors or both. For example, in other implementations, yield allocation weights are based on video or captured images of the size of the ear on the extraction plate or the size of the plant, where the size of the plant correlates with the mass of the plant that correlates with the grain mass. In yet other implementations, these weights are determined based on other sensed characteristics of the plants being harvested by the 820 harvester.
[00206] As in more detail above, in a mode of operation, the yield allocation module 52 additionally or alternatively uses a yield estimation mechanism based on different power characteristics of each of the different components 835 across a width of harvester crop cultivation 820 as sensed by 836 sensors, in which yield allocation weights for different plants in different geo-referenced regions are based on the sensed actual power characteristics and / or differences in the sensed power characteristics of the different components through cultivation width. For example, the combine 820 may be harvesting a first geo-referenced region with a first row unit or a group of row units and a second geo-referenced region at the same time with a different second row unit or a different second group of row units. Because the first geo-referenced region provides a higher crop yield than the second geo-referenced region, the power consumed or otherwise used to harvest crops in the first geo-referenced region will in many cases be greater than the power consumed or then used to harvest crops in the second geo-referenced region. As a result, the power consumed or used by components of the first row unit or first group of row units to harvest crops in the first geo-referenced region is likely to be greater than the power consumed or employed by components of the second row unit or the second group of row units to harvest crops in the second geo-referenced region. The combine harvester 820 uses sensors 836 across the crop width to sense a power characteristic associated with each of the different components through the harvest head and applies different yield allocation weights to different geo-referenced regions based on the effective power characteristic and / or a relationship between the sensed power characteristics of the different components of the different row units or groups of individual row units.
[00207] The aggregate performance module 50 comprises software, code, circuitry and / or program logic providing instructions for directing the processor 32 to determine an aggregate performance for each measurement interval based on signals received from the aggregate performance sensor 28. In an implementation, the aggregate throughput for each measurement interval is based on signals received from a gamma-ray attenuation sensor, impact plate sensors, flow sensors, load sensors and / or optical sensors.
[00208] Yield allocation module 52 comprises software, code, circuitry and / or program logic providing instructions for directing processor 830 to allocate portions of aggregate yield to a particular measurement range for each of at least two geo regions -references that were traversed by the combine 820 before the particular measurement interval, where the allocation is based on different amounts of time for crops to move to the aggregate yield sensor 820 after being initially separated from the growth medium or soil to the aggregate yield sensor 28. In the example shown, the time for crops to travel from the aggregation site to the aggregate yield sensor 28 is the same for crops harvested from each of 834 combine head portions. However, due to or different travel distances and / or different transport speeds, reflected by the different travel times for a the different transverse portions, crops removed by different transverse portions of the collector 34 during a particular measurement interval arrive at the aggregate yield sensor 28 at different times after the measurement interval has ended. The yield allocation module 52 allocates the aggregate yield value for the measurement interval to different geo-referenced regions that were traversed or interacted with crop removal portions 40 before the measurement interval.
[00209] In addition, as indicated by blocks 314 and 316 of method 300 (shown in Figure 4), the income allocation module 52 performs such aggregate income allocations based on income weights or region weights as determined based on in differences in income estimates from one geo-referenced region to the next. Such yield estimates or region weights are based on one or more of the above-described income estimation mechanisms. The yield mapping module 56 comprises software, code, circuitry and / or program logic providing instructions for directing the 830 processor to map the allocation of aggregate yield to the different geo-referenced regions traversed by the combine 820. In one implementation, yield mapping 56 registers or stores yield maps for the different geo-referenced regions in a data store 58. Data store 58 comprises a portion of memory data storage 828. In one implementation, in addition to storing income maps yield for different geo-referenced regions, data storage 58 also stores additional data such as aggregate yield for different measurement intervals to be previously detected characteristics of the plant that are detected during the harvest of these plants or that are detected in time prior to coupling the plants by the combine 820, such as during herbicide, insecticide or fertilizer application, cultivation or suspended or aerial collection of crop data. As noted above, in different implementations, data storage 58 is loaded by the combine 820, at a remote location from the combine 820 and / or is distributed through different locations.
[00210] Although the present invention has been described with reference to exemplary embodiments, professionals skilled in the art will recognize that changes can be made in the form and details without departing from the spirit and scope of the claimed matter. For example, not every element shown in the drawings is necessary and one or more elements can be omitted. Although different exemplary embodiments may have been described as including one or more elements providing one or more benefits, it is envisaged that the described elements may be interchanged with one another or alternatively be combined with each other in the exemplary embodiments described or elsewhere alternative embodiments. As the technology of the present invention is relatively complex, not all changes in technology are predictable. The present invention described with reference to the exemplary embodiments and given in the following claims is clearly intended to be as wide as possible. For example, unless specifically noted to the contrary, claims citing a single particular element also encompass a plurality of such particular elements.

权利要求:
Claims (15)
[0001]
1. Yield allocation method, comprising: receiving a first signal indicating an aggregate yield from a combine (22) measured by an aggregate yield sensor (28) during a measurement interval, the combine (22) comprising a head (516 ) with crop removal portions (40) and crop conveyors (42, 44) to transport the crop from the crop removal portions (40) to an aggregate yield sensor (28); receiving a second signal indicating a plurality of geo-referenced regions (70, 72, 74), through which a harvester (22) traveled before the measurement interval; allocate a portion of the aggregate yield to each of at least two geo-referenced regions (70, 72, 74) based on different travel times for crops to route the crop removal portions (40) to the sensor aggregate income (28); and issue allocations of aggregate income portions; characterized by the fact that the travel times for the crops to travel from the crop removal portions (40) to the aggregate yield sensor (28) are continuously or periodically detected or based on control signals establishing the speed of the conveyors. cultivation (42, 44).
[0002]
2. Method according to claim 1, characterized by the fact that it additionally comprises: sensing a harvester pitch (22); and allocating a portion of the aggregate income to the plurality of geo-referenced regions (70, 72, 74) based on the pitch.
[0003]
3. Method according to claim 1, characterized by the fact that it additionally comprises: sensing a combine roller (22); and allocating a portion of the aggregate income to the plurality of geo-referenced regions (70, 72, 74), based on the roll.
[0004]
4. Method according to claim 1, characterized by the fact that it additionally comprises: sensing an attribute of each of the plurality of plants harvested by the harvester (22); and allocating aggregate yield among the plurality of plants based on the sensed attribute of each of the plurality of plants.
[0005]
5. Method according to claim 1, characterized by the fact that it additionally comprises differently considering aggregate income allocations to the plurality of geo-referenced regions (70, 72, 74).
[0006]
6. Method according to claim 1, characterized by the fact that it further comprises: determining a plant attribute in the plurality of geo-referenced regions (70, 72, 74) before the plants engage the harvester (22); and allocating aggregate income among the plurality of plants additionally based on the determined attribute of each of the plurality of plants.
[0007]
7. Method according to claim 1, characterized by the fact that the geo-referenced regions (70, 72, 74), to which the aggregated yield portions of the measured range are allocated are part of a bifurcation format.
[0008]
8. Method according to claim 1, characterized by the fact that the allocation of the portions of the aggregate yield of the measured range to the plurality of geo-referenced regions (70, 72, 74) is based on a distance covered by a crop harvested up to be sensed by the aggregate yield sensor (28), transport speed applied by each transport subsystem and forward speed of the combine head (22).
[0009]
9. Yield allocation apparatus, comprising: a combine (22) with a collector (422) with crop removal portions (40) and crop conveyors (42, 44) to transport the crop from the crop removal portions ( 40) for an aggregate yield sensor (28), which is adapted to emit signals indicating an aggregate yield collected by the harvester collector (422) during a measurement interval; a location identifier (26) to identify a plurality of geo-referenced regions (70, 72, 74) that the harvester (22) traveled through before the measurement interval; and a processor (32) programmed to: determine the aggregate yield of the signals emitted by the sensor (28) during the measurement interval and to allocate the aggregate yield to the plurality of geo-referenced regions (70, 72, 74) based on different travel times for crops sensed by the aggregate sensor (28) during the measurement interval to traverse the crop removal portions (40) for the aggregate sensor (28); and to issue cultivation allocations for each of the plurality of geo-referenced regions (70, 72, 74); characterized by the fact that the processor (32) is programmed to allocate the aggregate yield to the plurality of geo-referenced regions (70, 72, 74) based on travel times so that the crops cover the crop removal portions ( 40) for the aggregate yield sensor (28); in which the travel times are continuously or periodically detected or based on control signals establishing the speed of the cultivation transporters (42, 44).
[0010]
Apparatus according to claim 9, characterized in that it comprises a second sensor (740, 744) for sensing transport speed across a transverse width of the harvester (22), in which the different travel time is determined with based on the sensed transport speed.
[0011]
11. Apparatus according to claim 9, characterized by the fact that it additionally comprises a second sensor for sensing a harvester pitch (22), in which the processor allocates a portion of the aggregate yield to at least one geo-referenced region that does not the primary geo-referenced region based additionally on the pitch.
[0012]
Apparatus according to claim 9, characterized in that it additionally comprises a second sensor for sensing a combine roller (22), in which the processor allocates a portion of the aggregate yield to at least one geo-referenced region that does not the primary geo-referenced region based additionally on the roll.
[0013]
Apparatus according to claim 9, characterized by the fact that it additionally comprises a second sensor for sensing an attribute of each of a plurality of plants harvested by the harvester (22), in which the processor allocates the aggregate yield among the plurality of plants based additionally on the sensed attribute of each of the plurality of plants.
[0014]
14. Apparatus according to claim 9, characterized by the fact that the processor is configured to: receive a plant attribute, the attribute acquired before the combine (22) is engaged with the plants; and allocating the aggregate yield among the plants further based on the attribute received from the plurality of plants.
[0015]
15. Apparatus according to claim 13, characterized by the fact that the aggregate yield is allocated between sets of plants, each set being harvested by a different transverse portion of the harvester (22) and / or in which the sensed attribute comprises a condition resulting from the impact of a portion of the plant with the combine (22).

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法律状态:
2016-04-19| B03A| Publication of an application: publication of a patent application or of a certificate of addition of invention|

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2018-10-02| B06F| Objections, documents and/or translations needed after an examination request according art. 34 industrial property law|

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2019-09-03| B06U| Preliminary requirement: requests with searches performed by other patent offices: suspension of the patent application procedure|

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2020-08-04| B09A| Decision: intention to grant|

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2020-09-29| B16A| Patent or certificate of addition of invention granted|Free format text: PRAZO DE VALIDADE: 20 (VINTE) ANOS CONTADOS A PARTIR DE 24/09/2015, OBSERVADAS AS CONDICOES LEGAIS. |

优先权:

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申请号 | 申请日 | 专利标题

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US201462054187P| true| 2014-09-23|2014-09-23|

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US62/054187|2014-09-25|

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US14/616,571|US9903979B2|2014-09-23|2015-02-06|Yield estimation|

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US14/616571|2015-02-06|

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US14/822848|2015-08-10|

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US14/822,848|US10126282B2|2014-09-23|2015-08-10|Yield estimation|

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