DETECTION METHOD OF TOMATO LEAF DISEASES BASED ON IMPROVED ATTENTION MECHANISM

The precise detection and recognition are the premise in accurate prevention and control of tomato diseases. To improve the accuracy of tomato diseases recognition model, nine kinds of sick leaves images including tomato target spot bacteria in Plant Village and healthy leaves images were used. A new attention mechanism module called CBAM-II was created by changing the serial connection between Channel and Spatial attentions of CBAM to parallel connection, and then the results of two modules were added together. CBAM-II had been verified to be effective and universal in the convolutional neural network model. The accuracy of MobileNet-V2 with CBAM-II model was 99.47%, which had increased by 1.13%, 0.93%, 0.7%8 and 1.06 % respectively comparing with MobileNet-V2 model, MobileNet-V2 plus Channel attention module, MobileNet-V2 plus Spatial attention module, and CBAM attention module. Furthermore, the accuracy of AlexNet, Inception-V3 and ResNet50 model has increased 1.73, 0.15 and 0.33 % respectively when the CBAM-II module was added. Results showed that the proposed module CBAM-II created in this experiment is more effective in MobileNet-V2 model for tomato diseases recognition, and could solve interference problems resulted from the serial connection. Additionally, the accuracy of four convolutional neural network models including Mobilenet-V2, AlexNet, Inception-V3 and ResNet50 model had all increased when the CBAM-II module was added, which represented the good universality of CBAM-II module. The results could provide technical support in accurate detection and control of tomato diseases.