Agri-Vision AI Powered Farm Segmentation

This project applies deep learning-based semantic segmentation in orchard mapping.The general objective is the accurate detection and localization of tree canopies based on UAV images, under a variety of conditions such as changing seasons, age of trees, and weed cover levels. U-Net architecture is used in training annotated datasets for automatic tree canopy segmentation, which generates masks related to canopy size, shape, and planting gaps, thus helping with precision agriculture. This approach improves decision-making capability in irrigation, pesticide application, and yield estimation. Using advanced computer vision techniques, the project provides a scalable, efficient solution for modern agricultural systems