Precision farming practices have been implemented in recent years in order to effectively modify farming practices by location within fields in order to maximize yield and economic return. Existing mapping technology is capable of displaying various maps of agricultural application and yield data. However, there is a need in the art for systems and methods for more effectively displaying such yield data, particularly during operations in the field.
Planter Data Collection System
Referring now to the drawings, wherein like reference numerals designate identical or corresponding parts throughout the several views,
Turing to
Continuing to refer to
Turning to
Continuing to refer to
Harvest Data Collection System
A harvest data collection system 700 is illustrated in
The grain height sensor 710 preferably comprises a sensor configured and disposed to measure the height of grain being lifted by the clean grain elevator. The grain height sensor 710 is preferably mounted to the sides of a clean grain elevator housing adjacent the location where grain piles are lifted vertically before reaching the top of the clean grain elevator. It should be appreciated that the grain height sensor 710 is not required for operation of the harvest data collection system 700 or the yield sensor assembly 790.
The moisture sensor 720 preferably comprises a sensor disposed to measure the moisture of grain being lifted by the clean grain elevator. For example, in some embodiments the moisture sensor 720 comprises a capacitive moisture sensor such as that disclosed in U.S. Pat. No. 6,285,198, the disclosure of which is hereby incorporated by reference herein in its entirety.
The GPS receiver 730 preferably comprises a receiver configured to receive a signal from a GPS or similar geographical referencing system. The global positioning receiver 730 is preferably mounted to the top of the combine 7.
The processing board 750 preferably comprises a central processing unit (CPU) and a memory for processing and storing signals from the system components 710, 720, 790, 730 and transmitting data to the monitor 50. The monitor 50 is preferably mounted inside a cab of the combine 7.
Monitoring Methods
Correlations
In operation, a first monitoring system (e.g., the planter monitoring system 600) preferably collects data during a first operation (e.g., a planting operation) and stores data (e.g., spatial planting data) collected during the first operation. A second monitoring system (e.g., the harvest data collection system 700) preferably collects data during a second operation (e.g., a harvesting operation) and stores data (e.g., spatial harvest data) collected during the second operation. During the second operation, the second monitoring system preferably displays visual and numerical correlations between the data collected during the first operation and the data collected during the second operation.
One such visual correlation between data collected during first and second agricultural operations is illustrated in
The completed planting map window 150 preferably includes a map layer 155 comprising display tiles 140. Each display tile 140 preferably includes map blocks 122, 124, 126 representing live planting data (e.g., hybrid type) associated with the location of the block. The spatial extent of each display tile 140 is preferably circumscribed by a unique geo-referenced boundary (e.g., a rectangular boundary); depending on the geo-referenced area displayed by the map layer 155, only a portion of any given display tile 140 may be displayed in the map layer 155 and the map window 150. The pattern, symbol or color of each map block corresponds to a legend 110 preferably displayed in the completed planting map window 150. The legend 110 preferably includes a set of legend ranges (e.g., legend ranges 112, 114, 116) including a pattern, symbol or color and a corresponding value range. In
The live yield map window 160 preferably includes a map layer 165 comprising yield map polygons 132, 134, 136 (or blocks similar to those used in the planting maps described herein) corresponding to ranges 182, 184, 186 of a yield legend 180. As the combine traverses the field, a combine annotation 12 preferably indicates the current location of the combine within the map. Annotations 15 preferably indicate the locations of each combine row unit when using a combine having a header (e.g., a corn header) configured to harvest a crop in discrete rows. An annotation 170-2 preferably remains at the same position with respect to the map boundary as the orientation and zoom level of map window 160 are manipulated.
A second correlation between data collected during first and second agricultural operations is illustrated in
A third correlation between data collected during first and second agricultural operations is illustrated in
Each correlation chart preferably includes an “Unknown” row in which harvest data is accumulated for locations which could not be satisfactorily associated with harvest data; e.g., where yield was measured at a location associated with multiple populations. A common example of such multiple associations may occur when one set of combine header row units is harvesting an area planted at a first population while another set of combine header row units is harvesting an area planted at a second population. Each correlation chart preferably includes a “Totals” row in which all the harvest data is accumulated for each subset of planting data including the “Unknown” subset. In other embodiments, the correlation charts are replaced and/or supplemented with visual correlations such as bar charts or scatter plots.
In addition to population and seed hybrid, other correlation embodiments similar to those above may correlate other planting data including planting depth, planting downforce, planting temperature, and planting moisture.
Data Access and Correlation Methods
Referring to
Turning to
At step 430, the monitor 50 preferably identifies any map tiles that have not been rendered as a desired bitmap or bitmaps.
At step 435 the monitor 50 preferably renders the identified map tiles as a bitmap or bitmaps. In a preferred embodiment, each bitmap comprises a 256 by 256 pixel bitmap, each pixel having a value corresponding to a value or range of values in a data set within the map tile, and the coordinates of each pixel corresponding to a geo-referenced location. As a representative example, a population data set in the map tile is rendered as a population bitmap in which each range of population is assigned a unique color index. As another representative example, a hybrid (seed variety) data set in the map tile is rendered as a hybrid bitmap in which each hybrid type or index is mapped to a color index value. The generated bitmaps are preferably stored in the memory cache of the monitor 50.
At step 440, the monitor 50 preferably converts each swath location (received at step 410) to a bitmap space coordinated in the map tile with which the swath location was associated at step 420. At step 445, the monitor 50 preferably obtains bitmap color values at each swath location bitmap coordinate. At step 450 the monitor 50 preferably stores the bitmap color values in an array for each data packet received (i.e., for all the swath locations in the data packet).
At step 460, the monitor 50 preferably determines the usability of data in each array. In a preferred embodiment, the monitor 50 determines whether the percentage of swath locations successfully associated with a color value, which is associated with a seed type, in the bitmap (e.g., the population bitmap) exceeds a threshold percentage, e.g. 90%. If the threshold is not met, the data in the array is preferably ignored or added to a “Bad” data set not used for display or correlation purposes by the monitor 50.
At step 470, the monitor 50 preferably determines a combined planting data value applicable to all the swath locations represented in the array. As an example, the population bitmap color values for each swath location are averaged such that the combined planting data value comprises an average value of all the swath locations represented in the array. As another example, the hybrid bitmap color values at each swath location are preferably used to identify a hybrid combination applicable to the entire combine head; for example, an “A” hybrid combination if each swath location was planted with seed variety A, a “B” hybrid combination if each swath location was planted with seed variety B, and an “A/B” hybrid combination if some swath locations were planted with seed variety A and others with seed variety B.
If no desired combination exists for a planting data set, then at step 472 the monitor 50 preferably ignores that planting data set or adds it to an “Unknown” data set. For example, if the hybrid data set in a given array includes a combination of hybrids not corresponding to any hybrid combination recognized by the monitor 50 (i.e., existing in a list of combinations stored in the memory of the monitor), then the hybrid data in that array is preferably ignored or added to an “Unknown” hybrid data set.
At step 475, the monitor 50 preferably associates the combined planting data value determined at step 470 with a planting data set comprising multiple ranges of planting data values. For example, in an illustrative embodiment an averaged population value of 30,010 seeds per acre is associated with a planting data set containing all population values between 30,000 seeds per acre and 30,500 seeds per acre.
At step 480, the monitor 50 preferably adds the harvest metric from the data packet to a cumulative harvest metric in the planting data set associated with the combined planting value. For example, in an illustrative embodiment a yield measurement (e.g., grain mass flow rate or bushels per acre) in a data packet having an averaged population value of 30,010 seeds per acre is added to an accumulated yield value associated with a planting data set containing all population values between 30,000 seeds per acre and 30,500 seeds per acre.
At step 490, the monitor 50 preferably displays a correlation (i.e., one of the visual or numerical correlations described above) between planting data sets (e.g., ranges of population) and cumulative harvest metrics (e.g., total harvested bushels per acre in each range of population).
The foregoing description is presented to enable one of ordinary skill in the art to make and use the invention and is provided in the context of a patent application and its requirements. Various modifications to the preferred embodiment of the apparatus, and the general principles and features of the system and methods described herein will be readily apparent to those of skill in the art. Thus, the present invention is not to be limited to the embodiments of the apparatus, system and methods described above and illustrated in the drawing figures, but is to be accorded the widest scope consistent with the spirit and scope of the appended claims.
This application is a Continuation of prior U.S. patent application Ser. No. 14/475,431, entitled AGRICULTURAL SPATIAL DATA PROCESSING SYSTEMS AND METHODS, filed Sep. 2, 2014, which claims the benefit of U.S. Provisional Application No. 61/872,291 entitled AGRICULTURAL SPATIAL DATA PROCESSING SYSTEMS AND METHODS, filed Aug. 30, 2003, the contents of which are incorporated herein by reference for all purposes.
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20170337642 A1 | Nov 2017 | US |
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61872291 | Aug 2013 | US |
Number | Date | Country | |
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Parent | 14475431 | Sep 2014 | US |
Child | 15674938 | US |