Research on automatic identification system of tobacco diseases |
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| Authors: | Yuanyuan Shao Guantao Xuan Yangyan Zhu Yanling Zhang Hongxing Peng Zhongzheng Liu |
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| Affiliation: | 1. College of Mechanical and Electronic Engineering, Shandong Agricultural University, Taian, China;2. Shandong Province Key Laboratory of Horticultural Machinery and Equipment, Taian, China;3. College of Mathematics and Informatics, South China Agricultural University, Guangzhou, China |
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| Abstract: | In order to improve recognition accuracy of tobacco diseases, an identification method based on multi-feature and genetic algorithms optimizing BP neural network was proposed. First, Otsu method was used to obtain disease location information and GrabCut function was initialized for extracting diseased area effectively. Second, colour moments, disease contour and GLCM were used to get colour, multi-contour and texture features. Once again, BP neural network was optimized by genetic algorithm, and the optimal initial weights and thresholds were obtained, which shortened the training time and improved the accuracy of disease identification. Finally, BP neural network model for tobacco diseases diagnosis was established with the mobile client as input and the user services as output. The field experiment showed that the method could diagnose eight types of tobacco diseases effectively and automatically. The average recognition accuracy rate of selected tobacco diseases was about 92.5%. |
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| Keywords: | Tobacco disease interactive segmentation multi-feature genetic algorithm BP neural network |
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