Rice Disease Detection

Rice disease detection research focuses on developing automated systems to identify various rice leaf diseases from images, aiming to improve crop yields and reduce economic losses caused by these diseases. Current research heavily utilizes deep learning, particularly convolutional neural networks (CNNs) such as YOLOv5 and modified MobileNet architectures, often incorporating techniques like transfer learning and image augmentation to enhance accuracy and efficiency. These advancements leverage computer vision to provide rapid and accurate disease diagnosis, assisting farmers in timely intervention and potentially mitigating significant crop production losses globally.