New method for rice disease identification based on improved deep residual shrinkage network. Issue 1 (31st December 2023)

• Record Type: Journal Article
• Authors: Lu, Yang; Lin, Liyuan; Zhang, Xinmeng; Liu, Wanting; Guan, Chuang
• Is Part Of: Systems science & control engineering. Volume 11:Issue 1(2023)
• Publication Date: 2023-12-31
• Subjects: Rice disease -- improvement of residual shrinkage network -- lightweight attention mechanisms -- image recognition -- deep learning; System theory -- Periodicals; Automatic control -- Periodicals; 003.05
• DOI: 10.1080/21642583.2023.2177770
• Languages: English
• ISSNs: 2164-2583

Abstract

Abstract : A new method with an improved deep residual shrinkage network is proposed to address the problems of subtle differences in spot characteristics among different rice diseases and low recognition rate under noise interference. First, to reduce the number of network parameters as well as arithmetic cost and increase the nonlinearity of the model, the InceptionA module is embedded in the original network, and the convolutional kernels in the original residual structure are replaced by multiple small-sized convolutional kernels. Second, in order to strengthen the spot features, Convolutional Block Attention Module (CBAM) lightweight attention mechanism is introduced to achieve more effective information extraction. Exponential Linear Units (ELU) and Focal loss function are introduced to jointly guide the model training during the network training process, and 10-fold cross-validation method is used. The proposed InceptionA and CBAM-based DRSN (ICDRSN) obtains 98.89% mean average precision, 98.65% accuracy and 98.68% recall for three rice leaf disease data. Among them, the recognition accuracy is improved by 2.6%, 3.34%, 1.86%, and 2.23% compared with the Densenet, Shufflenet, Mobilenet, and Resnet models, respectively. These results verify that the ICDRSN model is stable, reliable, accurate, fast, and has satisfactory generalization ability.