Research On Recognition Algorithm Of Citrus Diseases And Pests Based On Deep Learning

Citrus diseases and pests are important factors affecting the yield and quality of citrus.In this thesis,the deep learning algorithm is used to identify common diseases and pests of citrus,which is convenient for timely management of citrus plants,reducing the losses caused by diseases and pests,and also alleviates the time-consuming and laborious problem of traditional manual detection.The main research work is as follows:(1)In this thesis,the harm parts of citrus diseases and pests are mainly located on citrus leaves and fruits.On the leaves,anthracnose,canker disease,huanglong disease and leaf miner are mainly studied.On the fruits,canker disease and black spot disease are mainly studied.By collating multiple public citrus diseases and pest data sets as well as self-collected data sets,13,061 images were obtained.The original background of each image was retained for labeling,and then the data set was reasonably divided.(2)By comparing the recognition effects of various common target detection algorithms on citrus diseases and pests and integrating multiple evaluation indexes,YOLOX-s was finally selected as the benchmark model for further improvement.Improvement 1,in order to enhance the generalization ability of the model,an appropriate data enhancement strategy was selected according to the experimental data set in this thesis.With Mosaic data enhancement only,the m AP of the model was increased by 0.64%.Improvement 2: In order to make the network pay more attention to the feature information of the target to be detected,different attention mechanisms were added to the output feature m AP of the YOLOX-s backbone network.Finally,the ECANet attention mechanism increased the map of the model by 0.52% while increasing the number of parameters.Improvement 3: In order to improve the regression accuracy of IOU Loss positioning Loss function,multiple positioning loss functions were compared and analyzed.Finally,GIOU Loss was adopted to improve the m AP of the model by 0.96%.In order to solve the problem of unbalanced positive and negative samples and difficult samples in the data set,various confidence Loss functions were compared and analyzed,and finally Varifocal Loss was adopted to improve the m AP of the model by 0.91%.Finally,multiple effective improvement points are combined together,and most of them are not as good when used together as when used separately.Only Mosaic+GIOU Loss+Varifocal Loss is used together,and the m AP of the model is maximized,which increases by 1.29%.Compared with the model with larger parameters in YOLOX,the improved model has narrowed the performance gap,and the lightweight deployment of the model can be realized under the condition that certain performance is guaranteed,which is conducive to the revitalization of the citrus industry and the modernization of agriculture.