Row-Specific Yield Estimation Using Field Data Layer Segmentation
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Solution Overview
Problem
Existing agricultural data processing systems struggle to handle the large volumes of plant-by-plant data required for precise yield estimation without exceeding data processing budgets, particularly for crops like corn, wheat, or soybeans, which result in inefficiencies in data management and yield prediction.
Innovation Solution
A method utilizing multiple data layers, including moisture, planting date, seeding rate, and seedbed/furrow quality, collected via sensors and image processing, is transmitted to a central server for row-specific yield estimation, conserving data processing resources and enabling precise yield prediction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If plant-by-plant data collection is implemented for yield estimation, then measurement precision is improved, but data processing capacity is exceeded
Solution Approach 1:
The patent divides the field into management zones based on GPS coordinates and aggregates plant data at the zone level rather than processing individual plant data across the entire field. This segmentation reduces the total data volume requiring processing while maintaining yield estimation precision through zone-specific analysis.
Solution Approach 2:
The patent transitions from two-dimensional plant-by-plant data to three-dimensional spatial zoning by incorporating GPS coordinates and creating management zones. This dimensional change allows data aggregation at multiple levels (plant, row, zone, field) thereby reducing processing complexity while preserving measurement precision.
2Measurement precision
If data is collected for all plants in the field, then measurement precision is improved, but loss of time increases due to processing large datasets
Solution Approach 1:
By segmenting the field into management zones and aggregating data at the zone level, the patent reduces the number of individual plant records that must be processed. This segmentation maintains yield estimation accuracy through zone-specific analysis while significantly reducing data processing time.
Solution Approach 2:
The patent applies partial action by collecting and processing data for representative samples within management zones rather than every single plant. This approach provides sufficient yield estimation precision for agricultural decision-making while avoiding the excessive time required to process complete plant-by-plant datasets.
3Productivity
If crop inputs are increased to improve yield, then productivity is improved, but loss of substance increases
Solution Approach 1:
The patent enables local quality by providing yield and performance data specific to different management zones within the field. This allows farmers to optimize crop input application rates and timing for each zone based on its specific productivity characteristics, thereby improving overall yield while reducing total substance usage by avoiding uniform over-application across the entire field.
Data Source
AI summary
In one embodiment, obtained moisture data, planting data, seeding rate and seeding depth, and planted seedbed/furrow quality (e.g., first through fourth data layers associated with select data spear zones distributed throughout the field to conserve data processing resources) are applied, inputted or transmitted wirelessly to a central server, with an electronic data processing device that is configured to estimate a yield of the crop at a row position basis throughout the field.