Crop Segmentation

Crop segmentation, the process of identifying and delineating individual crops within images, is crucial for precision agriculture, enabling optimized resource allocation and improved yield prediction. Current research emphasizes the use of deep learning models, including U-Net architectures and variations incorporating Kolmogorov-Arnold networks (KANs) and attention mechanisms, to analyze high-resolution satellite and UAV imagery, often leveraging techniques like semi-supervised learning and data augmentation to address data scarcity. These advancements improve the accuracy and efficiency of crop monitoring, facilitating better decision-making in farming practices and contributing to sustainable agricultural management. Furthermore, research is exploring the use of foundation models and domain generalization techniques to improve the robustness and applicability of crop segmentation across diverse environments and crop types.