• 中文核心期刊
  • CSCD来源期刊
  • 中国科技核心期刊
  • CA、CABI、ZR收录期刊

基于物联网的害虫智能监测系统设计与实现

Design and Field Tests of Intelligent Pest Monitoring System based on Internet of Things

  • 摘要:
      目的  针对传统害虫监测手段存在时耗长、人工成本高、数据质量不高等问题,将性诱捕技术与物联网技术相结合,开发基于物联网的害虫智能监测系统,实现对目标害虫的自动计数。
      方法  应用诱芯与高压电网相结合进行害虫诱捕,采用红外传感器进行害虫计数,通过4G网络进行数据传输。基于.net平台开发害虫监测Web管理网站、害虫监测APP、数字植保微信公众号等配套软件系统,用户可以通过电脑、手机APP、微信等多终端远程浏览查询数据。
      结果  以蔬菜重要害虫斜纹夜蛾为例,通过在厦门同安、三明尤溪的蔬菜基地的田间试验结果显示,厦门同安试验点的诱捕效果为276.14%,自动计数准确率为93.52%;三明尤溪试验点诱捕效果为162.60%,自动计数准确率为81.59%,表明该自动监测系统的害虫诱捕率和识别准确率均较高。
      结论  开发的害虫智能监测系统实现了害虫测报的自动化和智能化,提高了害虫监测的效率,在害虫预测预报中具有广阔的应用前景。

     

    Abstract:
      Objective  To overcome the shortcomings of time-consuming, labor-intensive and inaccurate conventional data collection of pest monitoring at vegetable fields, a system utilizing the internet of things technology was developed and tested.
      Method  By coupling the use of sex pheromone to trap pests with the application of internet of things, a means to automatically count target pests in the field was designed. A pheromonal lure combined with a high voltage power grid was installed in vegetable fields to trap and kill pests for accurate insect counting. Infrared sensors were used to detect the presence of pests, and the collected data transmitted through a 4G network. Utilizing the .net platform, pest monitoring management website and APP, WeChat official digital plant protection account, and other supporting software were developed for easy access by users to browse and query data remotely with computer, mobile phone, WeChat connection or other terminals. One of the major pests infesting vegetables, Spodoptera litura (Fabricius), was targeted in the experiments held at the vegetable fields in Tong'an, Xiamen and Youxi, Sanming.
      Result  The insect trapping effects were 276.14% in Tong'an and 162.60% in Youxi. The counting accuracy of the system were 93.52% in Tong'an and 81.59% in Youxi. Both the trapping rate and counting accuracy delivered by the system in the tests were considered acceptable.
      Conclusion  The newly designed intelligent pests monitoring system automatically and accurately monitored the pests in the vegetable fields. It could be used to generate information for infestation forecasting.

     

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