Recognition of Tomato Leaf Diseases Based on DIMPCNET

Abstract

The identification of tomato leaf diseases is easily affected by complex backgrounds, small differences between different diseases, and large differences between the same diseases. Therefore, we propose a novel classification network for tomato leaf disease, the Dense Inception MobileNet-V2 parallel convolutional block attention module network (DIMPCNET). To begin, we collected a total of 1256 original images of 5 tomato leaf diseases and expanded them to 8190 using data enhancement techniques. Next, an improved bilateral filtering and threshold function (IBFTF) algorithm is designed to effectively remove noise. Then, the Dense Inception convolutional neural network module (DI) was designed to alleviate the problem of large intra-class differences and small inter-class differences. Then, a parallel convolutional block attention module (PCBAM) was added to MobileNet-V2 to reduce the impact of complex backgrounds. Finally, the experimental results show that the recognition accuracy and F1-score obtained by DIMPCNET are 94.44% and 0.9475. The loss is approximately 0.28%. This method is the most advanced and provides a new idea for the identification of crop diseases, such as tomatoes, and the development of smart agriculture.