Weed Segmentation

Weed segmentation in precision agriculture aims to automatically identify and delineate weeds from crops in images, enabling targeted herbicide application and optimizing resource use. Current research focuses on improving the accuracy and efficiency of deep learning models, particularly convolutional neural networks (CNNs) like YOLO and UNET, often enhanced by techniques such as data augmentation using GANs and active learning to reduce annotation costs. These advancements are driven by the need for large, diverse datasets representing various crop types, weed species, and growth stages, with recent work highlighting the importance of addressing class imbalance and data redundancy. Ultimately, accurate weed segmentation promises significant improvements in crop yield and sustainability through reduced herbicide use and optimized resource management.