Research On Recognition Of Wheat Main Leaf Diseases Based On Deep Learning Using RGB Image

Accurate identification of wheat diseases is helpful for early detection of diseases and implement reasonable prevention and control measures,thereby reducing crop yield losses and pesticide usage.Recognition of crop diseases based on machine vision has made remarkable achievements.However,this method requires experts to manually extract features from a small number of images based on experience.The number and characterization capabilities of such features are limited,and it is difficult to describe the variability of disease symptoms under actual field conditions.In recent years,the convolutional neural network based on deep learning has been used to extract the features of massive data and has been widely studied in plant disease recognition.The convolutional neural network can automatically extract local and global features layer by layer from the image.The number of features is large and the expression ability is strong,which can comprehensively describe the disease information in the real environment.In this paper,a large number of wheat leaf diseases(mainly including powdery mildew,leaf rust,and stripe rust)and healthy leaf images are obtained through consumer-grade cameras,and a method for identifying wheat leaf diseases based on convolutional neural networks is constructed.First,a two-stage training strategy based on migration learning is proposed,then Res Net18 is improved to construct a lightweight model named ghost-Res Net18,and finally a mobile application for disease recognition is deployed on a smartphone.The specific results are as follows:(1)To deal with the over-fitting problem of convolutional neural network caused by the small scale of the collected wheat disease dataset in the field,a two-stage training method based on transfer learning is proposed.This strategy obtains a pre-trained model by pre-training on the auxiliary data set Plantvillage,and then transfers the model parameters to the wheat disease data set through the fine-tuning method of transfer learning,which retraining from partial parameters to overall parameters in two stages.The current six advanced convolutional neural networks(VGG16,Dense Net121,Inception v3,Res Net50,Efficient Net b6,Mobilenetv2)are used for modeling,which using independent data to validate the model and using statistic indicators such as recognition accuracy,parameter amount,and single image recognition time to compare the performance of six networks.The results show that the two-stage training can effectively solve the over-fitting problem caused by insufficient data and improve the accuracy of disease recognition.Among the six network structures,the highest classification accuracy is Inception v3(92.53%),but the model parameter is large(83.2 M),and it takes 18.31 ms to recognize a picture on the GPU.The network with least parameter is Mobilenetv2(13.63 M),its recognition speed ranks second(7.55 ms),but the recognition accuracy rate is only 86.87%.VGG16 achieves the fastest recognition speed(4.64 ms),which may be due to its lower network complexity.The remaining medium and large-scale convolutional neural network model parameters are30.87-98 M,and the single picture recognition time is 12.53-20.31 ms.(2)In view of the large memory consumption and slow recognition speed due to large amount of parameters based on medium and large-scale convolutional neural network,this research proposes an improved Res Net18 structure.This architecture improves the convolution method of the network through ghost convolution to reduce the parameter number of the convolution layer in the model,which is named ghost-Res Net18 in this study.ghost-Res Net18 is compared with the most advanced,efficient,lightweight convolutional neural network(Mobilenetv2,Mobilenetv3,Ghost Net,Shufflenet v2)and original Res Net18.The results show that the ghost-Res Net18 network model proposed in this paper has 22.06 M parameters and a recognition accuracy of 93.74%.It only takes 4.26 ms to recognize a single image on the GPU.Compared with the original Res Net18(42.83 M),the parameter amount of ghost-Res Net18 is reduced by 48.49%.Compared with the other five two-stage training convolutional neural networks,the recognition accuracy is 0.61%-6.87%higher than other those using two-stage training.In terms of recognition speed,the improved ghost-Res Net18 in this article takes the least recognition time.(3)In order to realize the possibility of disease recognition by mobile devices,this research has formed a mobile application for disease recognition in Android mobile phone based on convolutional neural network,by deploying a wheat disease recognition model on smart phone devices.The application can identify diseases even without Internet connection.Farmers can use the Android mobile phone application to realize intelligent diagnosis without any knowledge of the disease,monitor the disease in real time and adopt reasonable prevention and treatment methods.