Tuning U-Net architecture for Weed Detection, Annotation, Mapping and Classification

Weed detection is an important application in modern agriculture that enhances crop yield and minimizes the environmental footprint of herbicide application. Weeding manually or blanket spraying with herbicides are traditional weed management methods that are labor and environment resource intensive. Recent advances in deep learning and computer vision have made autonomous weed detection systems feasible, with the U-Net architecture being a strong candidate for semantic image segmentation tasks. In this paper, we present a fine-tuned U-Net model that can effectively segment field images to isolate crops from weeds. We use transfer learning and minimal pre-trained U-Net adaptation to achieve superior accuracy and training performance even with limited labeled agricultural data. The system involves weed image annotation and mapping, and multi-class classification to facilitate accurate localization and recognition of weeds at the pixel level. Our method not only enhances segmentation accuracy (with 95% in our experiments) but also facilitates environmentally friendly farming with optimal herbicide application and minimized crop damage. The proposed system illustrates the use of deep learning in real-world agricultural application scenarios and has potential extensions to autonomous weeding systems.