Crop Disease

Crop disease detection is crucial for ensuring food security and optimizing agricultural yields. Current research heavily utilizes machine learning, particularly convolutional neural networks (CNNs) like YOLO and MobileNetV3, and support vector machines (SVMs), often incorporating attention mechanisms and chromatic analysis for improved image-based disease identification. These models are being developed for integration into mobile applications and deployed on low-power devices like the Jetson Nano for real-time field applications, leveraging drone imagery and smartphone-captured images. This work aims to provide farmers with accessible and accurate tools for early disease detection, enabling timely interventions and minimizing crop losses.