Crop Constituent Sensing Using Vegetative Index Subregions
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Solution Overview
Problem
Current crop constituent sensors on agricultural machines provide low resolution in geographic location identification, leading to imprecise subsequent agricultural processes like fertilizer application due to the large geographic area associated with each measurement, exacerbated by increasing harvester header widths.
Innovation Solution
An agricultural system that uses a vegetative index map to estimate crop constituent values by correlating vegetative index values with sensed crop constituent values, distributing these values to subregions and generating weighted crop constituent values for precise action signals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a single crop constituent sensor is used to measure the entire field, then the device complexity is low, but the measurement precision and spatial resolution are insufficient
Solution Approach 1:
The patent divides the field into multiple subregions and distributes sensor measurements across these subregions. Instead of using one sensor for the entire field, the system segments the measurement task across multiple spatial zones, allowing each subregion to have its own constituent value profile. This resolves the contradiction by maintaining relatively simple sensor hardware while achieving high spatial resolution through intelligent data distribution and processing.
Solution Approach 2:
The patent adds a spatial dimension to the measurement system by geo-referencing sensor data to specific locations and subregions. Rather than just measuring constituent values, the system maps these values across the field's geographic space, creating a two-dimensional resolution enhancement that improves measurement precision without proportionally increasing sensor complexity.
2Productivity
If the harvester header width is increased to improve productivity, then the productivity increases, but the measurement precision and spatial resolution of crop constituents deteriorate
Solution Approach 1:
The patent segments the wide header width into multiple subregions, each with its own constituent value measurements and distributions. By dividing the broad measurement zone into smaller spatial units, the system maintains high harvesting productivity with wide headers while recovering spatial resolution through subregion-specific data processing and weighted averaging techniques.
Solution Approach 2:
The patent applies local quality by generating specific constituent value profiles for each subregion rather than a single average for the entire header width. This allows different parts of the wide header to have tailored measurements and distributions, maintaining measurement precision across the expanded productivity-capable header width by treating each location with locally-appropriate data processing.
3Measurement precision
If distributed constituent values are used for each subregion, then the measurement precision improves, but the device complexity and data processing requirements increase
Solution Approach 1:
The patent implements feedback mechanisms where constituent measurements from one pass inform and adjust the distribution models for subsequent passes. The system uses measured constituent values to refine subregion definitions, update distribution algorithms, and improve weighted averaging calculations. This feedback loop allows the system to achieve high measurement precision while managing data processing complexity through iterative optimization rather than requiring overly complex initial processing architecture.
Data Source
AI summary
A crop constituent value is sensed by a crop constituent sensor on an agricultural machine. The crop constituent value is distributed among subregions covered by the agricultural machine. A vegetative index-estimated crop constituent value is obtained for each of the subregions. A weighted crop constituent value is generated for each subregion based upon the distributed constituent value for each subregion and the vegetative index-estimated constituent value for that subregion. An action signal is generated based upon the weighted crop constituent value for the subregion.