Adaptive Crop Yield Prediction Using Dynamic Weighting
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Solution Overview
Problem
Current methods for predicting crop yield are not accurate and efficient, as they rely on insufficient data analysis from satellite imagery and fixed weighting factors, leading to inconsistent and inaccurate predictions.
Innovation Solution
A system and method that utilizes satellite imagery to generate vegetation indices, combines them with crop data and supplemental data through a masking component, and employs a multivariate regression component to adjust weighting factors for improved crop yield prediction over time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If fixed weighting factors are used in crop yield prediction, then the system is simple to operate, but prediction accuracy deteriorates over time
Solution Approach 1:
The patent transforms fixed weighting factors into dynamic, adaptive weighting factors that automatically adjust based on actual crop yield data. The system continuously learns from historical data and modifies the importance (weighting) of different vegetation indices and input parameters, enabling the prediction model to adapt to changing agricultural conditions and improve accuracy over time without manual intervention.
Solution Approach 2:
The patent implements a feedback mechanism where actual crop yield data is fed back into the system to continuously refine and adjust the weighting factors. This closed-loop system compares predicted yields with actual yields and uses the discrepancy to optimize future predictions, ensuring the model remains accurate as conditions change while maintaining automated operation.
2Measurement precision
If insufficient data analysis from satellite imagery is used, then the processing time is reduced, but prediction accuracy deteriorates
Solution Approach 1:
The patent divides the complex satellite imagery analysis into multiple segments by generating several different vegetation indices (such as NDVI, EVI, SAVI) from the same image data. Each index captures different aspects of vegetation health and characteristics. This segmentation allows the system to analyze multiple features simultaneously without requiring multiple separate image acquisitions, thereby improving prediction accuracy while managing processing time through parallel computation of different indices from a single dataset.
3Measurement precision
If multiple vegetation indices are generated, then prediction accuracy is improved, but computational complexity increases
Solution Approach 1:
The patent creates a universal prediction framework that can handle multiple vegetation indices and various input parameters through a single adaptive model structure. The system is designed to process diverse data types (different vegetation indices, weather data, soil data) using the same underlying algorithm and weighting mechanism, reducing the need for separate processing pipelines for each data type and thereby managing computational complexity while maintaining the ability to leverage multiple indices for improved accuracy.
Data Source
AI summary
A device includes an image data receiving component, a vegetation index generation component, a crop data receiving component, a masking component and a multivariate regression component. The image data receiving component receives image data of a geographic region. The vegetation index generation component generates an array of vegetation indices based on the received image data, and includes a plurality of vegetation index generating components, each operable to generate a respective individual vegetation index based on the received image data. The crop data receiving component receives crop data associated with the geographic region. The masking component generates a masked vegetation index based on the array of vegetation indices and the received crop data. The multivariate regression component generates a crop parameter based on the masked vegetation index.