Rice Disease Identification System Using Lightweight MobileNetV2

Abstract

Rice is one of the main food crops in China, and rice diseases have become an important factor influencing the increase in food production losses in China. Traditional manual identification of rice diseases is time-consuming and labor-intensive. Machine learning algorithms have improved this problem and have been applied to the field of smart agriculture. The convolutional neural network (CNN) in deep learning has a significant effect on rice disease recognition relying on the characteristics of automatically extracting features. Aiming at five major rice diseases such as sheath blight, rice blast, bacterial leaf blight, rice smut and brown spot, this paper proposed a rice disease identification system using lightweight MobileNetV2. The identification results are uploaded and saved to the cloud database. Based on the lightweight model MobileNetV2, the system uses the channel pruning method to further compress the model. Compared with the original model, the memory usage has been reduced by 74%, the number of floating-point operations per second (FLOPS) has been reduced by 49%, the number of parameters has been reduced by 50%, and the accuracy of rice disease identification has increased by 0.16% to 90.84%.