Harvester Yield Data Correction for Overlapping Passes
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Solution Overview
Problem
Agronomic data accuracy is compromised due to errors in sensor measurements and data processing assumptions, particularly when a harvester traverses the same portion of a farmable region multiple times during harvest, leading to erroneous crop yield calculations that can mislead farm management decisions.
Innovation Solution
A method to identify and distinguish between 'first-pass' and 'second-pass' data collected by a harvester, allowing for the correction of inaccuracies by adjusting agronomic parameter values based on spatial and temporal data, and optionally removing or labeling second-pass data to prevent contamination in downstream analyses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a harvester traverses the same portion of a farmable region more than once during harvest, then complete harvest coverage is achieved, but measurement accuracy deteriorates due to erroneous low yield values from subsequent passes
Solution Approach 1:
The system performs preliminary identification of multiple traversals using spatial data before yield calculation occurs. By detecting overlapping harvest paths in advance, the system can flag and correct erroneous measurements from subsequent passes, preventing contamination of the final yield data rather than reacting after the error occurs.
Solution Approach 2:
Spatial data serves as an intermediary element that mediates between the raw yield measurements and the final corrected yield values. The system uses spatial coordinates to identify when the harvester has previously traversed a location, acting as a mediator to distinguish first-pass accurate measurements from second-pass erroneous measurements and apply appropriate corrections.
2Device complexity
If data processing tools assume the harvester does not traverse the same portion more than once, then data processing is simplified, but measurement accuracy deteriorates when multiple passes occur
Solution Approach 1:
The system performs preliminary analysis of spatial data to identify multiple traversals before the yield calculation process. This preliminary detection step allows the system to prepare correction factors or flags in advance, so that when yield data is processed, the corrections can be applied automatically without significantly increasing processing complexity.
Solution Approach 2:
The system changes the parameter set used in data processing by incorporating spatial coordinates alongside yield measurements. This additional parameter enables the detection of multiple traversals through spatial overlap analysis, allowing the system to distinguish between first-pass and second-pass data and apply appropriate weighting or correction factors to maintain accuracy.
3Quantity of substance
If second-pass data is included in downstream analysis, then data completeness is maintained, but data quality deteriorates due to propagation of erroneous low yield values
Solution Approach 1:
The system applies local quality control by treating first-pass and second-pass data differently based on their known accuracy characteristics. Instead of uniformly processing all data points, the system identifies specific geographic regions where multiple traversals occurred and applies localized correction factors or exclusion criteria only to those affected areas, preserving overall data completeness while eliminating localized errors.
Solution Approach 2:
Spatial data acts as an intermediary that enables the system to differentiate between high-quality first-pass measurements and low-quality second-pass measurements. By using spatial coordinates to trace harvester paths, the system can identify which data points require correction or exclusion, maintaining data completeness for statistical purposes while ensuring reliability through quality-aware processing.
Data Source
AI summary
Inaccurate agronomic data may be identified and corrected by obtaining agronomic data indicating areas of sub-regions of a farmable region and value for the sub-regions, wherein the value for each sub-region includes an aggregate value of an agronomic parameter for the sub-region and/or a ratio of the aggregate value to the area of the sub-region; identifying a part of a first sub-region that overlaps a part of a second sub-region; determining whether the second sub-region is associated with a first pass or a second pass by a sensor through the overlapping parts; and if the second sub-region is associated with the second pass, determining an adjusted value for the second sub-region based on the value for the first sub-region, the value for the second sub-region, the area of the first sub-region, the area of the second sub-region, and/or an area of the overlapping parts.