Weed Detection

Weed detection research focuses on developing automated systems, primarily using computer vision and deep learning, to identify and locate weeds in agricultural settings for improved precision weed management. Current efforts concentrate on enhancing model efficiency through semi-supervised learning techniques, optimizing performance across various spectral bands and image acquisition speeds, and addressing challenges posed by imbalanced datasets and diverse weed species using architectures like CNNs and transformers. This research is crucial for minimizing herbicide use, improving crop yields, and promoting environmentally sustainable agricultural practices.