Anomaly Detection System Using Image Tile Segmentation
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Solution Overview
Problem
Farmers face challenges in accurately assessing large agricultural plots to identify anomalies such as weeds, plant illnesses, and crop damage, relying on observational methods that are unreliable for increasing yields.
Innovation Solution
An image analysis system that captures high-altitude images, segments them into tiles, calculates indices like NDVI or SDVI, applies confidence masks, normalizes, and identifies anomalies using box averaging thresholds to generate a map of anomaly areas outlined by rectangles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If farmers rely on observational methods to assess farm land conditions, then the method is simple and requires minimal equipment, but the reliability and accuracy of anomaly detection deteriorates due to the large scale of farm land
Solution Approach 1:
The patent replaces manual observational methods with an automated image analysis system that uses high-altitude imagery and computational algorithms to detect anomalies. The system substitutes human visual inspection with machine-based image processing, thereby improving reliability while managing complexity through automation.
Solution Approach 2:
The system creates a digital copy of the farm land through high-altitude images, allowing analysis of the entire farm area without physically accessing each location. This copying approach enables reliable anomaly detection across large scales while keeping the physical inspection effort minimal.
2Productivity
If farmers manually observe their land to identify anomalies, then the equipment required is minimal, but the productivity and coverage area deteriorates due to the time-consuming nature of manual inspection
Solution Approach 1:
The patent segments the farm land into discrete image tiles that can be processed independently and in parallel. This segmentation allows the system to handle large areas efficiently by dividing the overall analysis task into smaller, manageable units, thereby improving productivity while managing computational complexity.
Solution Approach 2:
The system dynamically adjusts processing parameters such as tile size, overlap regions, and analysis thresholds based on the specific characteristics of the farm land and detected anomalies. This dynamic adaptation optimizes productivity for different farm sizes and conditions while managing system complexity through flexible parameter adjustment.
3Measurement precision
If the image analysis system processes the entire high-altitude image without segmentation, then the analysis is simpler to implement, but the measurement precision and anomaly identification accuracy deteriorates
Solution Approach 1:
The patent divides the high-altitude image into multiple overlapping tiles with specified pixel dimensions and overlap regions. This segmentation enables precise local analysis of each tile while maintaining context through overlaps, thereby improving measurement precision without overwhelming computational complexity.
Solution Approach 2:
The system applies localized analysis methods to each image tile, optimizing processing for specific regional characteristics. By treating each tile as a distinct unit with its own analysis parameters, the system achieves high precision in anomaly detection while managing overall complexity through modular processing.
Data Source
AI summary
An image analysis system including an image gathering unit that gathers a high-altitude image having multiple channels, an image analysis unit that segments the high-altitude image into a plurality of equally size tiles and determines an index value based on at least one channel of the image where the image analysis unit identifies areas containing anomalies in each image.