https://doi.org/10.4081/jae.2023.1544
Double-branch deep convolutional neural network-based rice leaf diseases recognition and classification
Published: 30 October 2023
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All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
Authors
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.
Copyright (c) 2023 the Author(s)
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