Research and Validation of Potato Late Blight Detection Method Based on Deep Learning

Abstract

Late blight, caused by phytophthora infestans, is a devastating disease in potato production. In severe cases, this can lead to potato crop failure. To rapidly detect potato late blight, in this study, a deep learning model was developed to discriminate the degree of potato leaf diseases with high recognition accuracy and a fast inference speed. It constructed a total of seven categories of potato leaf disease datasets in single and complex backgrounds, which were augmented using data enhancement method increase to increase the number of images to 7039. In this study, the performance of the pre-trained model for fine-grained classification of potato leaf diseases was evaluated comprehensively in terms of accuracy, inference speed, and the number of parameters. The ShuffleNetV2 2× model with better generalization ability and faster inference speed was selected and improved. Three improvement strategies were proposed: introducing an attention module, reducing the depth of the network, and reducing the number of 1 × 1 convolutions. Their effects on the performance of the underlying model were explored through experiments, and the best form of improvement was determined. The loss function of the improved model converged to 0.36. This was compared to the base model, which was reduced by 34.5%. In the meantime, the improved model reduced the number of parameters, FLOPs, and model size by approximately 23%, increased classification accuracy by 0.85%, and improved CPU inference speed by 25%. Deploying the improved model to the embedded device, the overall classification precision was 94%, and the average time taken to detect a single image was 3.27 s. The method provided critical technical support for the automatic identification of potato late blight.