Yield Map Generation Using Classification and Regression
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Solution Overview
Problem
Agricultural data used to determine crop yield often includes erroneous data points, leading to inaccurate yield values when using continuous functions, as existing technologies struggle to handle incomplete or imperfect data effectively.
Innovation Solution
A system environment comprising a client system, a network system, and measurement and observation systems that generate and filter indicators to create a yield map for an agricultural field, using machine learning algorithms to populate data cells with yield values, and visualize the results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If continuous functions are used to determine crop yield from agricultural data, then the method provides a complete coverage of the field, but the yield values become inaccurate due to erroneous data points
Solution Approach 1:
The patent divides the agricultural field into discrete grid cells, with each cell representing a specific area. Yield values are determined for each cell independently using classification and regression methods, allowing erroneous data points in some cells to be corrected without affecting the entire field's yield map completeness
Solution Approach 2:
The patent introduces an intermediary processing layer that uses classification methods to identify and handle erroneous data points, and regression methods to estimate yield values for cells with insufficient data. This intermediary layer acts as a mediator between raw agricultural data and final yield values, improving accuracy while maintaining data completeness
Data Source
AI summary
A yield model generates a yield map for an agricultural field. A measurement system generates measured indicators that are a measurement or quantification of crop yield in the agricultural field. An observation system generates observed indicators that are spatial agricultural datasets describing observed characteristics of the agricultural field. To generate the yield map, the yield model generates a field array representing the agricultural field. The yield model generates an input array and a yield array by mapping the observed indicators and measured indicators to cells of the field array, respectively. The yield model determines a yield value for each cell of the yield array not including a mapped indicator using information included in the corresponding cells of the input array. The yield model generates a yield map using the determined yield values and the yield values in the yield array.