Weed Datasets

Weed datasets are crucial for developing computer vision models to automate weed detection and management in agriculture, aiming to improve crop yields and reduce herbicide use. Current research focuses on addressing challenges like data scarcity and class imbalance through techniques such as semi-supervised learning, data augmentation (including generative models like Stable Diffusion), and optimized deep learning architectures (e.g., ResNet, YOLO). These advancements enable more accurate and efficient weed identification, leading to improved precision farming practices and potentially mitigating environmental impacts associated with traditional weed control methods.