Apple leaf disease recognition method with improved residual network

• Published in: Multimedia tools and applications Vol. 81; no. 6; pp. 7759 - 7782
• Main Authors: Yu, Helong, Cheng, Xianhe, Chen, Chengcheng, Heidari, Ali Asghar, Liu, Jiawen, Cai, Zhennao, Chen, Huiling
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.03.2022; Springer Nature B.V
• Subjects: Computer Communication Networks; Computer Science; Data Structures and Information Theory; Disease; Mathematical models; Multimedia Information Systems; Object recognition; Optimization; Parameters; Plant diseases; Special Purpose and Application-Based Systems; Technical services; Apple leaf disease; Identity mapping; Convolutional neural network; Residual network
• ISSN: 1380-7501; 1573-7721
• DOI: 10.1007/s11042-022-11915-2

The occurrence of apple diseases has dramatically affected the quality and yield of apples. Disease monitoring is an important measure to ensure the healthy development of the apple industry. Based on a residual network (ResNet50), this paper proposes an MSO-ResNet (multistep optimization ResNet) apple leaf disease recognition model. By decomposing the convolution kernel, updating the identity mapping method, reducing the number of residual modules, and replacing the batch normalization layer, the identification accuracy and speed of the model are improved, and the number of model parameters is reduced. The experimental results show that the average precision, recall, and F1-score of the proposed model for leaf disease identification are 0.957, 0.958, and 0.957, respectively. The parameter memory is 14.77 MB, and the recognition time of each image is only 25.84 ms. The overall performance of the proposed model was better than that of the other models. The proposed model in this paper has high recognition performance and strong robustness and can provide critical technical support for the automatic recognition of apple leaf diseases.