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

作　　者：史红栩 李修华[1,2] 李民赞 王伟[1] 温标堂[4] SHI Hongxu;LI Xiuhua;LI Minzan;WANG Wei;WEN Biaotang(School of Electrical Engineering,Guangxi University,Nanning 530004,China;Guangxi Key Laboratory for Sugarcane Biology,Nanning 530004,China;Key Laboratory of Modern Precision Agriculture System Integration Research,Ministry of Education,China Agricultural University,Beijing 100083,China;Guangxi Jie Jia Run Technology Co.,Ltd.,Nanning 530001,China)

机构地区：[1]广西大学电气工程学院,广西南宁530004 [2]广西甘蔗生物学重点实验室,广西南宁530004 [3]中国农业大学现代精细农业系统集成研究教育部重点实验室,北京100083 [4]广西捷佳润科技有限公司,广西南宁530001

出　　处：《华南农业大学学报》2020年第6期92-99,共8页Journal of South China Agricultural University

基　　金：国家自然科学基金(31760342);广西科技重大专项经费项目(桂科AA18118037)。

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。【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 5944 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.

关 键 词：香蕉病害 深度学习 GoogLeNet 图像识别 移动应用 迁移学习 

分 类 号：S126[农业科学—农业基础科学]