面向移动终端的农业害虫图像智能识别系统的研究与开发  

Research and Development of the Intelligent Identification System of Agricultural Pests for Mobile Terminals

作　　者：邵泽中 姚青[1] 唐健 李罕琼 杨保军 吕军[1] 陈轶[4] SHAO ZeZhong;YAO Qing;TANG Jian;LI HanQiong;YANG BaoJun;Lü Jun;CHEN Yi(School of Information and Technology,Zhejiang Sci-Tech University,Hangzhou 310018;State Key Laboratory of Rice Biology,China National Rice Research Institute,Hangzhou 310006;Jia Shan County Agricultural and Rural Bureau,Jiaxing 314100,Zhejiang;Tongxiang Agricultural Technology Extension Service Center Tongxiang 314500,Zhejiang)

机构地区：[1]浙江理工大学信息学院,杭州310018 [2]中国水稻研究所/水稻生物学国家重点实验室,杭州310006 [3]嘉善县农业农村局,浙江嘉兴314100 [4]桐乡市农业技术推广服务中心,浙江桐乡314500

出　　处：《中国农业科学》2020年第16期3257-3268,共12页Scientia Agricultura Sinica

基　　金：浙江省公益性项目(LGN18C140007);国家“863”计划(2013AA102402)。

摘　　要：【目的】农作物田间害虫种类繁多,存在种间相似和种内差异的现象,容易混淆。本研究开发一个面向移动终端的农业害虫图像智能识别系统,为广大农户和基层测报人员提供一个便捷准确的农业害虫智能识别工具。【方法】农业害虫图像智能识别系统包括装有系统APP的移动客户端、服务器和基于深度学习的农业害虫识别模型。APP是在Android环境下开发的,可安装于Android系统的移动设备中。APP包括登录模块、害虫信息查询模块、害虫智能识别模块、害虫地图标记模块和害虫专家远程鉴定模块,UI界面采用底部导航栏形式。移动终端与服务器间的信息交互采用HTTP协议,害虫采集地信息显示使用百度的Android地图SDK来实现,用户和害虫信息使用MySQL数据库进行保存。在相同训练集和测试集条件下,比较了不同深度卷积神经网络模型,筛选出基于DenseNet121的农业害虫识别模型具有最高的精准度和最低的虚警率。农业害虫识别模型的程序部署在阿里云远程服务器上,当服务器端接收到移动客户端上传的害虫图像时,运行害虫识别模型,识别结果通过服务器反馈给客户端,同时将上传的图像和识别结果保存在数据库中,便于害虫图像的追溯。【结果】当用户在农田遇到不认识的害虫时,可通过装有该系统APP的移动设备(如手机或平板)拍摄害虫图像,并上传到服务器,识别结果和害虫防治信息在1—2 s内反馈至用户移动终端的屏幕上,对识别结果不满意还可远程请求专家鉴定。该系统对66种常见农业害虫图像平均识别率为93.9%,平均虚警率为8.2%。【结论】面向移动终端的农业害虫图像智能识别系统实现了66种常见农业害虫信息查询、自动识别,害虫采集地的地图显示和专家远程鉴定等功能。为农民和基层测报人员提供了一个农业害虫便捷准确的自动识别工具,无需专家到田间【Objective】There are various species of pests in crop fields,and interspecific similarity and intraspecific difference are common in agricultural pests,which are easy to be confused.In this study,an intelligent system based on mobile terminals was developed to identify agricultural field pests.This system is an easy and intelligent tool of pest identification for peasants and pest forecasting technicians.【Method】The intelligent identification system of agricultural field pests consists of a mobile client with a system APP,a server and a pest identification model based on deep learning.The application(APP)can be installed in mobile devices with Android system and includes user registration,pest information inquiry,pest automatic identification,pest location information and remote expert identification.The UI interface in this APP uses the style of bottom navigation bar,the information exchange between mobile client and server adopts HTTP protocol and the SDK of Baidu Android map is used to mark the geographic information of pests.The information of users and pests is saved in MySQL database.In the same training and testing sets,different convolutional neural network models were developed to identify agricultural pests.The results showed that the DenseNet121 model achieved the highest precision and lowest false alarm rate.The pest identification model based on Dense Net121 was installed in Alibaba Cloud remote server.When the server received the images from the mobile clients,the identification model was performed.The identification results were feed back to clients from the server.All images and results were saved in database for being traced back in future.【Result】When the users met unidentified pests in crop fields,the users could collect pest images and upload them to the server by the APP installed in mobile clients,such as mobile phone or PAD.The identification results and pest control information would be fed back to the mobile clients in 1-2 seconds.If the results were unsatisfied,the user could

关 键 词：农业害虫 移动终端 云服务器 卷积神经网络 图像智能识别 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术] TP311.56[自动化与计算机技术—计算机科学与技术] S433[农业科学—农业昆虫与害虫防治]