技术与方法

广东省农区鼠类物联网智能监测系统的应用研究

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  • 1. 广东省农业科学院植物保护研究所杂草鼠害研究室, 植物保护新技术重点实验室, 广东 广州 510640;
    2. 广东省农业有害生物预警防控中心, 广东 广州 510500
姚丹丹,女,硕士,助理研究员,从事鼠类生态与防控技术研究工作,E-mail:gx-002@163.com

收稿日期: 2021-10-12

  网络出版日期: 2022-05-09

基金资助

2020年省级农业科技创新及推广项目(2020KJ113);国家科技基础资源调查专项(2019FY100302)

Study on application of IoT intelligent monitoring system for agricultural rodent pests in Guangdong province, China

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  • 1. Guangdong Provincial Key Laboratory of High Technology for Plant Protection, Weed and Rodent Damage Department of Plant Protection Research Institute, Guangdong Academy of Agricultural Sciences, Guangzhou, Guangdong 510640, China;
    2. Guangdong Agricultural Pest Early Warning and Control Center, Guangzhou, Guangdong 510500, China

Received date: 2021-10-12

  Online published: 2022-05-09

Supported by

Provincial Agricultural Science and Technology Innovation and Extension Project in 2020 (No. 2020KJ113); National Science and Technology Basic Resources Survey Project (No. 2019FY100302)

摘要

目的 初步探索鼠类物联网智能监测系统在广东省农区鼠害监测中应用的可行性,为精准防控鼠害提供科学依据。方法 将物联网智能监测终端布放在广东省南雄市珠玑镇监测点内进行实时监测,同时每月定期采用夹夜法调查鼠类种群密度并进行对比分析,采用SPSS 19.0软件进行回归和相关分析。结果 2019年9月-2020年10月鼠类物联网智能监测系统共监测到清晰的鼠形动物1 457只,智能识别的准确率达到95.26%;对比夹夜法与鼠类物联网智能监测,两者所获鼠密度的季节变化趋势基本吻合,但鼠类群落结构差异较大,主要是黄毛鼠和板齿鼠的数量比例差异较大,在夹夜法捕获的鼠类中,黄毛鼠所占比例高达71.69%,板齿鼠仅占4.22%;而鼠类物联网智能监测到的黄毛鼠占监测鼠数的50.41%,板齿鼠占21.97%;物联网智能监测结果显示,不同鼠种的空间分布具有一定的竞争性,数量高峰出现交错分布。结论 鼠类物联网智能监测方法可替代繁重的人工捕鼠作业,克服传统的夹夜法监测结果不稳定、准确率不高的弊端,可用于广东省农区鼠害监测,但其应用技术还有待进一步完善。

本文引用格式

姚丹丹, 黄立胜, 姜洪雪, 林思亮, 冯志勇 . 广东省农区鼠类物联网智能监测系统的应用研究[J]. 中国媒介生物学及控制杂志, 2022 , 33(2) : 273 -276 . DOI: 10.11853/j.issn.1003.8280.2022.02.020

Abstract

Objective To perform a preliminary study on the applicability of intelligent monitoring system based on internet of things (IoT) for agricultural rodent pests in Guangdong province, China, and to provide a scientific basis for accurate prevention and control of rodents. Methods Intelligent monitoring terminals were set up in Zhuji town, Nanxiong city of Guangdong province for real-time monitoring. Rodent population density was investigated using the night snap-trapping method every month for comparative analysis. SPSS 19.0 software was used for regression and correlation analyses. Results From September 2019 to October 2020, 1 457 rodents were detected by the IoT intelligent monitoring system, and the accuracy of intelligent recognition was 95.26%. The night snap-trap and the IoT intelligent monitoring showed a similar seasonal trend of rodent density but different community structures of rodents, mainly in the proportions of Rattus losea and Bandicota indica. Among the rodents captured by the night snap-trapping method, the proportion of R. losea was as high as 71.69% and the proportion of B. indica was only 4.22%. In contrast, the proportion of R. losea and B. indica monitored by IoT intelligent monitoring system was 50.41% and 21.97%, respectively. The IoT intelligent monitoring system showed that rodent species competed for distribution spaces and the maximum numbers appeared at different time points. Conclusion IoT intelligent monitoring can be used to replace manual rodent capture and overcome the low stability and accuracy associated with traditional night snap-trapping method. Although it can be used for rodent monitoring in agricultural areas of Guangdong province, the technology of IoT intelligent monitoring needs to be further improved.

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