Recognition of grape leaf diseases using MobileNetV3 and deep transfer learning  

作　　者：Xiang Yin Wenhua Li Zhen Li Lili Yi 

机构地区：[1]School of Agricultural Engineering and Food Science,Shandong University of Technology,Zibo 255000,Shandong,China [2]School of Artificial Intelligence,Nanjing University of Aeronautics and Astronautics,Nanjing 210016,China

出　　处：《International Journal of Agricultural and Biological Engineering》2022年第3期184-194,共11页国际农业与生物工程学报（英文）

基　　金：The authors acknowledge that this work was financially supported by the National Natural Science Foundation of China(Grant No.32171910);the Natural Science Foundation of Shandong Province(Grant No.ZR2020MC085);the Key R&D Project of Shandong Province(Grant No.2019JZZY010734);the Key R&D Project of Zibo City,Shandong Province,China(Grant No.2019ZBXC143).

摘　　要：Timely diagnosis and accurate identification of grape leaf diseases are decisive for controlling the spread of disease and ensuring the healthy development of the grape industry.The objective of this research was to propose a simple and efficient approach to improve grape leaf disease identification accuracy with limited computing resources and scale of training image dataset based on deep transfer learning and an improved MobileNetV3 model(GLD-DTL).A pre-training model was obtained by training MobileNetV3 using the ImageNet dataset to extract common features of the grape leaves.And the last convolution layer of the pre-training model was modified by adding a batch normalization function.A dropout layer followed by a fully connected layer was used to improve the generalization ability of the pre-training model and realize a weight matrix to quantify the scores of six diseases,according to which the Softmax method was added as the top layer of the modified networks to give probability distribution of six diseases.Finally,the grape leaf diseases dataset,which was constructed by processing the image with data augmentation and image annotation technologies,was input into the modified networks to retrain the networks to obtain the grape leaf diseases recognition(GLDR)model.Results showed that the proposed GLD-DTL approach had better performance than some recent approaches.The identification accuracy was as high as 99.84%while the model size was as small as 30 MB.

关 键 词：grape leaf diseases real-time recognition deep transfer learning MobileNetV3 

分 类 号：S66[农业科学—果树学]