Detecting and segmenting destructive anomalies in farmland from satellite images, improving time, efficiency, and crop yield.
🍅 AI-powered tomato classification system using ResNet-50 and color analysis to sort tomatoes into ripe, unripe, and damaged categories. Includes video frame extraction, batch processing, and pre-trained model with 95%+ accuracy.
Folder with code related to object detection in the CCTV cameras placed in the agricultural field and also down streaming for agricultural use-case
A comprehensive implementation of CBAM-STN-TPS-YOLO architecture for agricultural object detection, featuring convolutional block attention modules (CBAM), spatial transformer networks (STN), and thin plate spline (TPS) transformations. Includes cross-dataset evaluation on PGP, GlobalWheat, and MelonFlower datasets with statistical validation.
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