Detection and classification of pests and diseases in rice leaf images based on deep learning algorithms

Rice is one of the important food crops worldwide, but it is susceptible to various pests and diseases during its growth process, leading to yield reduction and even death. Therefore, it is very important to detect and categorize rice pests and diseases in a timely and accurate manner. In this paper, we use the dataset from Kaggle public database, which is divided into two categories of images: "normal leaves" and "infested by pests and diseases", and configure AlexNet, Vgg and MobileNetV2 models based on Pytorch framework to detect and classify rice pests and diseases in a timely and accurate manner. MobileNetV2 model was configured based on Pytorch framework to detect and classify pests and diseases in rice leaf images. Through the changes of the loss and Accuracy curves of AlexNet, Vgg and MobileNetV2 models, we found that all three deep learning models have good training effect and can well predict whether the rice leaf images are infested by pests and diseases. The final results show that the prediction accuracy of all three sets of deep learning models reaches more than 97%, and they can detect pests and diseases on rice leaf images very accurately. This means that we can use these models to quickly and accurately detect and categorize rice pests and diseases, so that timely measures can be taken to prevent the spread of pests and diseases and improve the yield and quality of rice.