Apple Leaf Diseases Recognition Based on an Improved Convolutional Neural Network

Sensors

Jun Zhang¹  Peng Chen¹  Wenyan Wang²  Qian Yan²  Bing Wang²  Baohua Yang³ 

[1] Co-Innovation Center for Information Supply & Assurance Technology, Anhui University, Hefei 230032, China;School of Electrical and Information Engineering, Anhui University of Technology, Ma’anshan 243032, China;School of Information and Computer, Anhui Agricultural University, Hefei 230036, China;

关键词: apple leaf diseases;    transfer learning;    deep learning;    convolutional neural networks;   

DOI  :  10.3390/s20123535

来源: DOAJ

【 摘 要 】

Scab, frogeye spot, and cedar rust are three common types of apple leaf diseases, and the rapid diagnosis and accurate identification of them play an important role in the development of apple production. In this work, an improved model based on VGG16 is proposed to identify apple leaf diseases, in which the global average poling layer is used to replace the fully connected layer to reduce the parameters and a batch normalization layer is added to improve the convergence speed. A transfer learning strategy is used to avoid a long training time. The experimental results show that the overall accuracy of apple leaf classification based on the proposed model can reach 99.01%. Compared with the classical VGG16, the model parameters are reduced by 89%, the recognition accuracy is improved by 6.3%, and the training time is reduced to 0.56% of that of the original model. Therefore, the deep convolutional neural network model proposed in this work provides a better solution for the identification of apple leaf diseases with higher accuracy and a faster convergence speed.

【 授权许可】

Unknown