Crop Mapping Model Using Synthetic Labels for Regional Adaptation
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Solution Overview
Problem
Current crop mapping algorithms require extensive manual ground data collection and are not adaptable to year-to-year changes or regional variations, making them time-consuming and ineffective for regions with diverse cropping patterns and climatic conditions.
Innovation Solution
A method and system for generating a labelled pixel dataset using remote sensing images across climatic seasons, which captures regional dependencies and localized phenological indicators, enabling the creation of region-specific Machine Learning models for accurate crop mapping, and continuously updating these models with agro-related historical information and phenological features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional ground truth data collection based ML approaches are used, then model accuracy for crop mapping is improved, but time consumption and manual effort increase significantly
Solution Approach 1:
The system performs self-labeling of training data by automatically generating synthetic ground truth labels through the crop mapping model itself. The model processes remote sensing images and generates predicted crop type labels, which are then used as training labels without requiring manual ground truth collection, thus eliminating the time-consuming manual labeling process while maintaining model accuracy through iterative self-improvement
Solution Approach 2:
The system creates synthetic copies of ground truth data by generating simulated crop mapping labels based on the model's predictions and available remote sensing data. These synthetic labels serve as proxies for actual ground truth data, allowing the model to be trained without extensive manual field data collection, thereby reducing time consumption while preserving mapping accuracy
2Measurement precision
If a model is created for one region, then it achieves good performance for that specific region, but it cannot be applied to other regions with different cropping patterns
Solution Approach 1:
The system develops a universal crop mapping model that can be applied across multiple regions with different cropping patterns and climatic conditions. The model uses region-agnostic features from remote sensing images and employs transfer learning to adapt to new regions without requiring complete retraining, thus achieving both region-specific accuracy and broad regional adaptability through a single multi-functional model
Solution Approach 2:
The system incorporates local adaptation mechanisms that allow the model to adjust to region-specific characteristics while maintaining its core functionality. The model learns local phenological indicators and cropping patterns specific to each region through transfer learning and fine-tuning, enabling it to achieve high accuracy in each local context without losing its ability to operate across different regions
3Quantity of substance
If manual ground data collection is performed, then comprehensive training data is obtained, but the process requires extensive manual workforce and is time-consuming
Solution Approach 1:
The system replaces the mechanical process of manual ground data collection with an automated computational approach. Instead of physically going to fields to collect ground truth data, the system uses remote sensing images processed by machine learning algorithms to automatically generate training labels, substituting human fieldwork with automated image analysis and synthetic label generation processes
4Reliability
If conventional ML models are used, then they work for stable conditions, but they require recreation when cropping patterns or climatic conditions change
Solution Approach 1:
The system implements a dynamic model that can adapt to changing cropping patterns and climatic conditions over time. The model uses continuous learning from new remote sensing data and employs transfer learning to adjust to temporal changes in agriculture practices and climate, maintaining reliability under stable conditions while automatically adapting when changes occur without requiring complete model recreation
Data Source
AI summary
Machine Learning models to be created for crop mapping for any region, require huge volumes of ground truth data requiring manual effort in generating region specific training dataset. Method and system for providing generalized approach for crop mapping across regions with varying characteristics is disclosed. The method provides automatic generation of a labelled pixel dataset representing cropping pattern of a Region of Interest (ROI) for building a ML crop mapping model for the ROI. The generated labelled pixel dataset captures regional dependency and localized phenological indicators for the ROI. ML crop mapping model is updated using a database, regularly updated for the set of crops and the plurality of features associated with each of the set of crops and corresponding the set of crops.