Recognition method of tomato leaf diseases based on convolutional neural network
Journal Title: Journal of Henan Agricultural University - Year 2024, Vol 58, Issue 2
Abstract
[Objective] The convolutional neural network was used to construct a crop disease recognition model to improve the recognition performance and solve the problems of low recognition performance and poor generalization effect of crop diseases. [Method] The data augmentation technology was used to increase the sample diversity. The focal loss was introduced to improve the model learning target, and the sample imbalance problem was solved. The recognition performance of the different convolutional neural network structures was analyzed and compared, and the reliability of the model was measured by class activation map generation technology. The effectiveness of the method was verified on the tomato leaf diseases dataset. [Result] After the application of data augmentation technology, the recognition accuracy of the model on simple background samples was increased by 1.0%, and that on complex background samples was increased by 12.5%. The focus loss improved the accuracy of the model by 0.1%. The recognition accuracy of the model was 99.8%, and the recall rate of various diseases was above 97.3%. The saliency map generated by the class activation map technology could effectively identify the key areas of the model in the recognition process. [Conclusion] This method can effectively solve the problem of unbalanced disease image samples and improve the generalization performance of the disease recognition model, and use the class activation map to analyze the reliability of the model, which can provide reference for the prevention and control of the tomato leaf diseases.
Authors and Affiliations
Junting LIU, Yuncheng ZHOU, Qiong WU, Xiongwei WU, Changyuan WANG
Keywords
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- EP ID EP769339
- DOI 10.16445/j.cnki.1000-2340.20240018.002
- Views 63
- Downloads 0
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