https://doi.org/10.4081/jae.2023.1544

Double-branch deep convolutional neural network-based rice leaf diseases recognition and classification

Vol. 55 No. 1 (2024)
    Published:30 October 2023
    Convolutional block attention module (CBAM), deep convolution neural network (DCNN), rice leaf diseases classification
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    Authors

    Xiong Bi
    School of Mathematical Sciences, Chongqing Normal University, Chongqing, China.
    Hongchun Wang
    wanghc@cqnu.edu.cn
    School of Mathematical Sciences, Chongqing Normal University, Chongqing, China.

    Deep convolutional neural network (DCNN) has recently made significant strides in the classification and recognition of rice leaf disease. The majority of classification models perform disease image recognitions using collocation patterns including pooling layers, convolutional layers, and fully connected layers, followed by repeating this structure to complete depth increase. However, the key information of the lesion area is locally limited. That is to say, in the case of only performing feature extraction according to the above-mentioned model, redundant and low-correlation image feature information with the lesion area will be received, resulting in low accuracy of the model. For improvement of the network structure and accuracy promotion, here we proposed a double-branch DCNN (DBDCNN) model with a convolutional block attention module (CBAM). The results show that the accuracy of the classic models VGG-16, ResNet-50, ResNet50+CBAM, MobileNet-V2, GoogLeNet, EfficientNet-B1 and Inception-V2 is lower than the accuracy of the model in this paper (98.73%). Collectively, the DBDCNN model here we proposed might be a better choice for classification and identification of rice leaf diseases in the future, based on its novel identification strategy for crop disease diagnosis.

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    How to Cite

    Bi, X. and Wang, H. (2023) “Double-branch deep convolutional neural network-based rice leaf diseases recognition and classification”, Journal of Agricultural Engineering, 55(1). doi: 10.4081/jae.2023.1544.
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