Crop Yield Estimation via Regional Segmentation and Multivariate Analysis
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Solution Overview
Problem
Agricultural practices face challenges in accurately predicting crop yields due to variations in growing conditions within fields, leading to inaccurate predictions and inefficient resource management.
Innovation Solution
The method involves identifying regions within a field with similar growing conditions through image processing and multivariate analysis, selecting representative data collection points, and using machine learning models to collect and analyze in-field data for more accurate yield predictions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional yield prediction methods are used without regional segmentation, then the prediction process is simpler, but the prediction accuracy deteriorates due to variations in growing conditions within fields
Solution Approach 1:
The patent divides the field into multiple regions based on growing conditions (soil type, topography, irrigation, etc.), and further segments each region into data collection zones with representative points. This hierarchical segmentation allows accurate capture of spatial variations in growing conditions while maintaining manageable data collection and analysis complexity.
Solution Approach 2:
The patent applies different analysis methods and data collection strategies to different regions based on their specific growing conditions. Each region is characterized by its unique combination of soil, topography, and management practices, allowing tailored prediction approaches for each local area rather than applying a uniform method across the entire field.
2Loss of information
If comprehensive in-field data collection is performed across the entire field, then the data completeness improves, but the data collection time and resources increase significantly
Solution Approach 1:
The patent extracts only the essential growing condition parameters relevant to yield prediction (soil type, topography, irrigation, fertilizer, pest control) from the entire field spectrum. By identifying and collecting only these key parameters at strategically selected representative points, the system achieves comprehensive growing condition coverage without the time and resource burden of exhaustive field-wide data collection.
Solution Approach 2:
The patent collects data at a sufficient number of representative points within each region to capture the essential growing condition variations, rather than attempting to collect data at every possible location. This partial action approach provides adequate information for accurate yield prediction while significantly reducing data collection time and resources compared to complete field coverage.
3Reliability
If multiple data collection points are selected within each region, then the representativeness of growing conditions improves, but the complexity of data processing and analysis increases
Solution Approach 1:
The patent performs preliminary clustering of the field into regions with similar growing conditions before selecting data collection points. This preliminary organizational action simplifies subsequent data processing by grouping similar data together and establishing a hierarchical structure that reduces the computational burden of analyzing individual data points across the entire field.
Solution Approach 2:
The patent identifies representative data collection points that serve as proxies for the entire region's growing conditions. By selecting points that accurately represent their respective regions, the system can infer regional characteristics from these copies/representatives, reducing the need to process and analyze data from every location while maintaining high reliability in growing condition assessment.
Data Source
AI summary
The present disclosure provides for crop yield estimation by identifying, via image processing, a field in which a crop is grown; identifying a plurality of regions within the field; identifying, by processing growth metrics via a model, a plurality of data collection points in the plurality of regions, wherein a given data collection point of the plurality of data collection points within a given region of the plurality of regions is identified by multivariate analysis as representative of growing conditions in the given region; receiving in-field data linked to the data collection points of the plurality; and predicting a yield for the crop in the field based on the in-field data.