Research On The Methods Of Identification And Lesion Segmentation Of Common Apple Leaf Diseases

Apple is the fruit with the largest yield in China,and disease is an important factor affecting the healthy development of apple industry in China,which seriously affects the yield and economic benefits of apple industry.The main diseases of apple are Alternaria leaf spot disease,powdery mildew,rust,black star disease,virus disease,silver leaf disease and so on.These diseases could cause leaf color change and even leaf shedding.A large number of leaf shedding will weaken the tree strength of the apple tree,reduce the resistance of the tree to diseases,and lead to the decline of fruit yield or quality.Therefore,the rapid and accurate identification of apple leaf diseases and disease degree is of great significance for the precise prevention and control of apple disease and the reduction of economic losses.In order to overcome the shortage of disease recognition based on image processing methods,which manually select image features,and their recognition model has poor transferability.In this paper,apple leaf disease images are taken as the research object,and the identification of apple leaf disease and semantic segmentation of apple leaf images based on deep learning are mainly studied.It provides technical support for automatic,efficient and accurate diagnosis of apple leaf diseases.The major research contents and conclusions of this paper are as follows:(1)XDNet deep learning network combining the advantages of Xception and DenseNet networks is proposed.Firstly,a dataset containing images of 5 common leaf diseases and healthy leaves was established,and the collected apple leaf disease dataset was expanded by using angle interference,light interference,noise interference and color space interference.Then,considering that Xception has fewer parameters and fast operation speed because of the usage of depth-wise separable convolutions,and the Dense block in DenseNet enable the network more powerful in feature reusing ability,we proposed XDNet network by incorporating dense connection modules into the deep layer of Xception network,Combined with the advantages of Xception network and DenseNet.All the networks were pre-trained on subset of Plant Village dataset,and then transferred to the collected apple leaf diseases dataset.Experimental results show that the proposed XDNet network has a disease recognition accuracy of 98.35%,which is better than Xception,Densenet,and other classic deep learning classification networks.The data augmentation and transfer learning technologies improved the classification performance and convergence speed of the model.(2)In order to introduce channel attentions into convolutional networks,SE-DEEP module to fuse SE(Squeeze and Excitation)module with depth-wise separable convolution was proposed.Then,SE-DEEP module was combined with Xception network to put forward the SE_Xception network.Then,SE_mini Xception network was designed to compress SE_Xception network through experimets.Since depth-wise separable convolution can be separated into the depth-wise convolution and pointwise convolution,this paper proposed a method to insert SE module into Xception network,which is different from those four methods for SE module integrated into CNN networks proposed in the paper which the SE module was proposed.In the proposed method,SE module is insert between the depth-wise convolution and pointwise convolution,then based on the deep fusion of SE module with Xception network,SE_Xception is proposed,and its disease recognition accuracy reaches 99.40%.Considering the need of disease recognition model deployment in mobile terminal,the model compression experiment was carried out by squeezing the depth and width of the network.By comparing different compression methods and different compression scales,SE_minixception with small number of parameters and better disease recognition performance is designed,its disease recognition accuracy reaches 97.01%,which is higher than Mobile Net and ShuffleNet.The network compression ratio of SE_minixception reaches 69:1,and it meets the requirements of the number of parameters and the amount of computation for network deployment in the mobile terminal.(3)Four sematic segmentation networks were compared for the sematic segmentation of apple leaf disease semantic segmentation task,including U-Net,Deeplabv3 +,PSPNet and Segnet,and the segmentation network for the apple leaf lesion segmentation was selected.Using transfer learning and data augmentation technology,the above four networks are tested under different hyperparameter combinations,and the best hyperparameter combinations is selected for each network.The experimental results show that U-Net has the best average performance in semantic segmentation of apple leaf images,its mean intersection over union reaches 93.21%,and Mobile Net V1 pretrained in Image Net dataset is the best backbone for UNet.(4)In order to add the global spatial attentions to the semantic segmentation network,the apple lesion segmentation network with global spatial context was built.TPM(Twill Pooling Module),DCPM(Double Cross Pooling Module)and DCMPM(Double Cross Mixed Pooling Module)modules were constructed to enable segmentation backbones to efficiently model long-range dependencies.The designed modules were integrated into U-Net,so that the DCPU-Net semantic segmentation network was designed.Based on the analysis of the advantages and possibilities to improve SPM(Strip Pooling Moudle)and MPM(Mixed Pooling Module)modules,the twill pooling module was constructed,and the DCPM module was proposed by combining TPM with SPM.On the basis of MPM,the twill pooling was integrated to get the DCMPM module.Each module was added to the U-Net network for experiment,and appropriate modules were selected for different locations of the designed DCPU-Net network.In the fusion experiment of U-Net with Mobile NetV1 as backbone network,the proposed TPM and DCPM modules have better performance than SPM,and the DCMPM module also performs better than MPM.The mean intersection over union of the designed DCPU-Net reached 95.46%,which was 2.09% higher than that of baseline U-Net.(5)The software framework for apple leaf disease identification and disease degree diagnosis was designed and developed.Uses B/S mode,Vue framework and HTML,CSS,Java script were used for the front-end design,Spring Boot framework and Java language were used for back-end design,and the proposed DCPU-Net was embedded to realize the apple disease identification and leaf lesion segmentation,and then diagnose the disease degree based on the segmentation result according to the national standard for disease degree diagnosis.The user can input the disease images of the sampling points in the designated orchard area,and the system can calculate the disease index of an orchard area,and provide the user with prevention and treatment suggestions.The system also has the functions of communication forum for fruit growers and information browsing of common diseases prevention and control.The main function tests show that the interface of the system is simple and easy to use,and the expected functions of disease identification,disease degree diagnosis and diseae index of orchar area can be realized.Test results show that system disease recognition accuracy is96.52%,and system disease degree diagnosis accuracy is 85.81%,the orchard area disease estimation module can realize the predicted function and can deal with various special cases.