Identification of tomato leaf diseases based on LMBRNet

Tomato disease image identification plays a very important role in the field of agricultural production. Aiming at the problems of large intraclass differences, small inter-class differences and difficult feature extraction of some tomato leaf diseases, this paper proposes an identification of tomato leaf diseases based on LMBRNet. Firstly, a comprehensive grouped differentiated residual (CGDR) is built,The multi-branch structure of CGDR is used to capture the diversified feature information of tomato leaf diseases in different dimensions and receptive fields. then, a multiple residual connection scheme is adopted,using residuals to connect all layers, to ensure the maximum information transmission between layers in the network and to solve the problems of network degradation and gradient disappearance in the network training process. Secondly,the visual enhancement effectively fuses the results obtained by three different downsampling strategies using average pooling, max pooling, and 1*1 convolution. Avoid the loss of information caused by downsampling and improve the accuracy of the network. Moreover, deep separable convolution is used to optimize the network structure, reduce the amount of model parameters and reduce the computational resources occupied by training and deploying the model.we found that the depthwise separable convolution with a kernel size of 1*1 can slightly improve the efficiency of the model under the premise that it has little effect on the number of model parameters. The application results of more than 8000 images show that the overall identification accuracy is about 99.7%,higher than ResNet50(97.48%),GoogleNet(98.96%) etc. conventional models. The parameter amount of LMBRNet is 4.1M. Less than ResNet50(23M),GoogleNet(5.7M) etc. conventional models. It is worth noting that the accuracy of LMBRNet(99.7%) is similar to that of InceptionResNetV2(99.68%), but the amount of parameters of LMBRNet(4.1M) is much lower than that of InceptionResNetV2(54M). Moreover, the parameter amount of LMBRNet (4.1M) is slightly lower than that of MobileNetV2(2.2M), but the accuracy rate of LMBRNet(99.7%) is higher than that of MobileNetV2(97.87%). LMBRNet was tested on RS, SIW, Plantvillage-corn public datasets, all obtained high recognition accuracy, 82.32% on RS, 88.37% on SIW and 97.25% on Plantvillage-corn, indicating that LMBRNet has good generalization. Compare LMBRNet with advanced methods. In four different classification tasks, the performance of LMBRNet is similar to ResMLP12 and DCCAM-MRNet, and the difference of recognition accuracy between LMBRNet and ResMLP12 and DCCAM-MRNet is not more than 1%. However, the parameters of LMBRNet (4.1M) are lower than ResMLP12 (14.94M) and DCCAM MRNet (22.8M). LMBRNet is compared with MobileNetV3, an advanced lightweight classification model. LMBRNet(88.37% on SIW,82.32% ON RS) is used on certain datasets and performs better than MobileNetV3S(83.76% on SIW,75 on RS) and MobileNetV3L(84.34 on SIW,73.39 on RS). The parameters of LMBRNet(4.1M) are lower than MobileNetV3L(5.4M) and slightly higher than MobileNetV3S(2.9M). This indicates that LMBRNet has good generality even though it has a small number of parameters.