Plant Disease

Plant disease detection is crucial for ensuring food security and optimizing agricultural practices. Current research heavily emphasizes automated disease identification using machine learning, particularly convolutional neural networks (CNNs) and vision transformers, often integrated with drone or satellite imagery for large-scale monitoring. These models are trained on diverse datasets, including both RGB and hyperspectral images, and are being refined to improve accuracy and efficiency, even on resource-constrained devices. The development of robust, accessible diagnostic tools has significant implications for improving crop yields, reducing pesticide use, and enhancing global food production.