Weed Recognition

Weed recognition, crucial for efficient and sustainable agriculture, leverages computer vision and deep learning to automate weed identification in crops. Current research focuses on improving the accuracy and efficiency of deep neural networks, such as convolutional neural networks (CNNs) and YOLO architectures, often addressing challenges like class imbalance and the need for large labeled datasets through techniques like semi-supervised learning and data augmentation using diffusion probabilistic models. These advancements enable the development of robotic weeding systems and precision agriculture technologies, ultimately reducing reliance on herbicides and improving crop yields.