Deep Learning Prediction of Agricultural Management Zones
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Solution Overview
Problem
Current methods for generating the Cropland Data Layer (CDL) are time-consuming and lack timely data, making it difficult for agricultural entities to access current-year growing season information, and there is a need for systems to predict CDL based on satellite imagery and detect agricultural management practices zones.
Innovation Solution
A computer-implemented method using deep learning convolutional neural networks trained on historical CDL and satellite imagery data to predict agricultural management zones within a current growing year, employing transfer learning techniques to configure networks for early layer and upper layer training, enabling the generation of predicted zones from current imagery data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If traditional CDL generation methods are used, then data accuracy is maintained, but data timeliness deteriorates (CDL released in Q1 of following year)
Solution Approach 1:
The system performs preliminary training of deep learning models using historical CDL data and satellite imagery before the current growing season. The models are pre-configured with transfer learning parameters from early layers, allowing them to make predictions during the growing season without requiring complete retraining, thus achieving timely CDL predictions while maintaining accuracy through continuous validation against ground truth data
Solution Approach 2:
The patent replaces the traditional mechanical/manual CDL generation process with automated deep learning neural networks that process satellite imagery data. This substitution enables rapid prediction of management zones and crop types directly from imagery, eliminating the time lag inherent in traditional methods while maintaining measurement precision through validated model outputs
2Measurement precision
If deep learning models are trained from scratch on current data, then prediction accuracy improves, but training time and computational resources increase
Solution Approach 1:
The system copies and reuses pre-trained model parameters from historical data and early network layers rather than training models from scratch. Transfer learning parameters from previously trained networks are instantiated in the current model, significantly reducing training time while maintaining high prediction accuracy through the reuse of validated feature extractors
Solution Approach 2:
The patent applies parameter changes through transfer learning, where parameters from early layers of pre-trained networks are preserved and only upper layers are retrained on current data. This selective parameter update approach maintains the beneficial features learned from historical data while adapting to current conditions, achieving accurate predictions with reduced computational resources and time
3Measurement precision
If manual verification of agricultural practices is performed, then detection accuracy is maintained, but labor requirements and costs increase
Solution Approach 1:
The system substitutes manual verification processes with automated deep learning models that detect agricultural management practices directly from satellite imagery. The neural networks automatically identify regenerative practices such as cover cropping, crop rotation, and conservation tillage, eliminating the need for labor-intensive field visits while maintaining detection accuracy through validated model performance
Solution Approach 2:
The system enables self-service verification where the deep learning models independently detect and verify agricultural practices without requiring human intervention. The models automatically process satellite imagery, identify management zones, and determine practice implementation status, allowing the system to verify its own work through internal validation mechanisms
Data Source
AI summary
A computer-implemented method for predicting a cropland data layer (CDL) for a current year includes: retrieving a first set of records from a historical CDL database, where the first set corresponds to sampled areas of a region taken over a period for a number of years; retrieving a second set of records from a historical imagery database, where the second set corresponds to the sampled areas of the region, the period, and the number of years; employing the second set as inputs to train a deep learning network to generate the first set; retrieving a third set of records from a current imagery database, where the third set corresponds to a prescribed region, and where the third set corresponds to the time period and the current year; and using the third set as inputs and executing the trained deep learning network to generate a predicted CDL for the current year.