Agricultural Field Boundary Segmentation Using Multi-Image Composites
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Solution Overview
Problem
Existing image processing systems for agricultural fields lack accuracy and consistency in identifying field boundaries, leading to incorrect crop metrics and decision-making based on inaccurate data.
Innovation Solution
A system and method that utilizes satellite imagery, a segmentation model, and composite calculations to generate precise field boundaries by masking, filtering, and prompting images, followed by segmentation and boundary definition using a model trained on diverse datasets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional image processing methods are used to identify field boundaries, then the processing is simpler and faster, but the accuracy and consistency of boundary identification deteriorates
Solution Approach 1:
The patent applies segmentation by dividing the image processing task into distinct stages: initial boundary detection, composite generation from multiple images, and refined boundary identification. The system segments the agricultural field into multiple image composites (e.g., red edge composite, NIR composite) and processes each separately before combining results, thereby improving measurement precision while managing complexity through structured decomposition
Solution Approach 2:
The patent transitions from two-dimensional spatial analysis to multi-dimensional processing by incorporating temporal dimension (multiple images captured at different times) and spectral dimension (multiple composites with different wavelength combinations). This dimensional expansion enables more accurate boundary identification by analyzing the same field from multiple perspectives simultaneously
2Measurement precision
If multiple image composites are generated and processed, then the boundary identification accuracy improves, but the processing time and computational resources increase
Solution Approach 1:
The patent applies preliminary action by pre-generating multiple image composites from the captured images before boundary identification is needed. These composites (red edge, NIR, green edge, blue edge) are prepared in advance and stored, allowing rapid retrieval and processing when boundary analysis is required, thus reducing real-time processing time while maintaining high accuracy
Solution Approach 2:
The patent merges multiple image composites and their respective boundary segmentations into a unified boundary identification result. By combining the strengths of different composites (e.g., red edge for vegetation boundaries, NIR for soil boundaries), the system achieves superior accuracy while distributing computational load across multiple specialized processing streams rather than one complex monolithic process
Data Source
AI summary
Systems and methods are provided for processing images related to boundaries. An example computer-implemented method includes accessing an image data set, which includes multiple images of an agricultural field; masking the image data set based on one or more criteria; calculating, by a computing device, one or more composites from the image data set; generating, by the computing device, using a model, a segmentation of each of the one or more composites, based on the one or more composites; and combining the segmentation(s) for the one or more composites into a field boundary for the agricultural field.