Development of an automatic monitoring system for rice light-trap pests based on machine vision
文献类型： 外文期刊
作者： Yao Qing 1 ; Feng Jin 1 ; Tang Jian 2 ; Xu Wei-gen 3 ; Zhu Xu-hua 4 ; Yang Bao-jun 2 ; Lu Jun 1 ; Xie Yi-ze 5 ; Yao Bo 1 ; Wu S 1 ;
作者机构： 1.Zhejiang Sci Tech Univ, Sch Informat Sci & Technol, Hangzhou 310018, Peoples R China
2.China Natl Rice Res Inst, State Key Lab Rice Biol, Hangzhou 310006, Peoples R China
3.Plant Protect Quarantine & Pesticide Management S, Hangzhou 310020, Peoples R China
4.Zhejiang Top Cloud Agri Technol Co Ltd, Hangzhou 310015, Peoples R China
5.Agr Technol Extens Ctr Shangyu, Shaoxing 312300, Peoples R China
6.Agr Technol Extens Ctr Keerqin, Keerqin 137713, Peoples R China
关键词： automatic monitoring system; light trap; rice pest; machine vision; image processing; convolutional neural network
期刊名称：JOURNAL OF INTEGRATIVE AGRICULTURE （ 影响因子：2.848； 五年影响因子：2.979 ）
ISSN： 2095-3119
年卷期： 2020 年 19 卷 10 期
页码：
收录情况： SCI
摘要： Monitoring pest populations in paddy fields is important to effectively implement integrated pest management. Light traps are widely used to monitor field pests all over the world. Most conventional light traps still involve manual identification of target pests from lots of trapped insects, which is time-consuming, labor-intensive and error-prone, especially in pest peak periods. In this paper, we developed an automatic monitoring system for rice light-trap pests based on machine vision. This system is composed of an intelligent light trap, a computer or mobile phone client platform and a cloud server. The light trap firstly traps, kills and disperses insects, then collects images of trapped insects and sends each image to the cloud server. Five target pests in images are automatically identified and counted by pest identification models loaded in the server. To avoid light-trap insects piling up, a vibration plate and a moving rotation conveyor belt are adopted to disperse these trapped insects. There was a close correlation (r=0.92) between our automatic and manual identification methods based on the daily pest number of one-year images from one light trap. Field experiments demonstrated the effectiveness and accuracy of our automatic light trap monitoring system.
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