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    史红栩, 李修华, 李民赞, 等. 基于深度学习的香蕉病害远程诊断系统[J]. 华南农业大学学报, 2020, 41(6): 92-99. DOI: 10.7671/j.issn.1001-411X.202004027
    引用本文: 史红栩, 李修华, 李民赞, 等. 基于深度学习的香蕉病害远程诊断系统[J]. 华南农业大学学报, 2020, 41(6): 92-99. DOI: 10.7671/j.issn.1001-411X.202004027
    shu
    SHI Hongxu, LI Xiuhua, LI Minzan, et al. Remote diagnosis system of banana diseases based on deep learning[J]. Journal of South China Agricultural University, 2020, 41(6): 92-99. DOI: 10.7671/j.issn.1001-411X.202004027
    Citation: SHI Hongxu, LI Xiuhua, LI Minzan, et al. Remote diagnosis system of banana diseases based on deep learning[J]. Journal of South China Agricultural University, 2020, 41(6): 92-99. DOI: 10.7671/j.issn.1001-411X.202004027
    shu

    基于深度学习的香蕉病害远程诊断系统

    Remote diagnosis system of banana diseases based on deep learning

      摘要
    • 摘要:
      目的  实现香蕉病害的远程诊断。
      方法  基于深度学习方法对香蕉作物的7种常见病害进行诊断。收集了5944幅健康及染病香蕉植株图像，按7∶1∶2分为训练集、验证集和测试集。利用迁移学习对GoogLeNet深度卷积神经网络训练获取诊断模型。进一步开发了包含手机移动应用程序(APP)和远程服务器的软件系统。
      结果  通过对比不同迭代次数及不同优化器，最终采用了MomentumOptimizer迭代10000次的模型，平均测试精度达到了98%。设计的APP能够就地获取香蕉图像，并通过网络与集成了诊断模型的远程服务器通信，实时获取诊断结果。
      结论  该病害诊断模型识别主要病害的精度高，在线诊断系统简单易操作，可快速有效地在线诊断香蕉常见病害，具有良好的应用前景。

       

      Abstract:
      Objective  To realize remote diagnosis of banana diseases.
      Method  Deep learning method was used to diagnose seven common diseases of banana plant. A total of 5 944 images of diseased and healthy banana plants were collected and divided into training set, validation set and testing set according to the ratio of 7∶1∶2. Transfer learning was used to train GoogLeNet which is a deep convolutional neural network for obtaining the diagnosis model. A software system including a mobile application (APP) and a remote server was further developed.
      Result  By comparing different iteration times and optimizers, the model of MomentumOptimizer with 10000 iteration times was finally selected, and the average test accuracy was 98%. The designed mobile APP could acquire banana images in situ, and communicate with the remote server which was integrated with a diagnosis model via the network to obtain diagnosis results in real time.
      Conclusion  The disease diagnosis model can identify the main diseases with high accuracy. The online diagnosis system is simple and easy to operate, it can diagnose common banana diseases online quickly and effectively, and therefore it has a wide application prospect.

       

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