Agricultural GPS Error Detection via Planting-Harvesting Data Matching
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Solution Overview
Problem
Inaccurate GPS data due to poor signal quality leads to errors in agricultural field measurements, causing incorrect correlation of yield values with treatment plans and locations, necessitating automated error detection and correction processes for precise agricultural data analysis.
Innovation Solution
A computer system that aggregates and processes planting and harvesting data to determine matching pairs of geo-location coordinates, calculates GPS error values, and presents these errors for correction, utilizing a server computer system to generate datasets and identify matching pairs based on proximity and timestamps.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If automated error detection processes are implemented, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The system performs self-validation by automatically comparing GPS coordinates against expected spatial relationships between agricultural operations. The error detection mechanism serves itself by using the operational data already collected to identify and flag potential GPS errors without requiring external intervention or complex additional hardware.
Solution Approach 2:
The system implements feedback loops where GPS coordinates from one operation are compared against coordinates from related operations (e.g., planting vs. harvesting). This feedback mechanism automatically identifies inconsistencies and triggers correction processes, improving measurement precision through iterative validation and adjustment.
2Productivity
If GPS data is collected for all field operations, then productivity is improved, but loss of information increases due to poor signal quality
Solution Approach 1:
The system converts harmful GPS signal errors into beneficial correction opportunities by automatically detecting inconsistencies in spatial relationships between operations. When GPS errors are detected, the system triggers validation processes that compare coordinates across multiple operations to identify and correct errors, transforming poor signal quality into opportunities for improved data accuracy.
Solution Approach 2:
The system performs preliminary validation of GPS data by comparing coordinates against expected spatial relationships before finalizing operational records. This preliminary action identifies potential errors early in the data collection process, allowing for correction before the erroneous data is used in yield calculations or other critical analyses.
Data Source
AI summary
In an embodiment, a server computer (“server”) identifies planting datasets of planting data values that correspond to separate planting passes in a field and harvesting datasets of harvesting data values that correspond to separate harvesting passes in the field, each planting data value and each harvesting data value including a location value. The server normalizes the planting datasets based on a heading direction of a first agricultural equipment, a row-unit shift of the first agricultural equipment, or a direct measurement of a first measurement device value. The server also normalizes the harvesting datasets based on a heading direction of a second agricultural equipment, a row-unit shift of the second agricultural equipment, or a direct measurement of a second measurement device value. The server further matches, after the normalizing, the planting datasets and the harvesting datasets using location values in corresponding planting data values and harvesting data values to generate planting-to-harvesting pairs. Finally, the server computes a set of location errors from each planting-to-harvesting pair of the planting-to-harvesting pairs.