Improved Lightweight Convolutional Networks for Classification of Grape Diseased Leaves
Aiming at the problem of low accuracy and efficiency of traditional manual detection of types of grape leaf diseases, a grape leaf disease image classification method with improved lightweight convolutional network model is proposed First, the ReLU activation function in the original network is r...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | zho |
| Published: |
Harbin University of Science and Technology Publications
2023-06-01
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| Series: | Journal of Harbin University of Science and Technology |
| Subjects: | |
| Online Access: | https://hlgxb.hrbust.edu.cn/#/digest?ArticleID=2208 |
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| Summary: | Aiming at the problem of low accuracy and efficiency of traditional manual detection of types of grape leaf diseases, a grape leaf disease image classification method with improved lightweight convolutional network model is proposed First, the ReLU activation function in the original network is replaced by the ELU activation function; second, a new fully connected layer is designed, and the global average pooling layer is replaced by the global maximum pooling layer, and the output layer is improved; and then the channel attention mechanism is embedded in the network. The data is preprocessed, and divided into training and test sets in the ratio of 4 In order to simulate realistic shooting situations, methods of data enhancement are used in training to expand the data, and then the weight parameters pretrained on ImageNet are migrated to the improved model The experimental results show that the accuracy of the improved grape leaf disease classification model (Grape-Xception) improved by 2.95 percentage points to 99.57% compared to the original model, and the size of the model was 81.38 MB.Compared with other network models, the accuracy of the model is substantially improved, and it provides an accurate and efficient method for rapid diagnosis and timely prevention and control of grape leaf diseases. |
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| ISSN: | 1007-2683 |