基于物联网的害虫智能监测系统设计与实现

邱荣洲; 赵健; 池美香; 梁勇; 陈世雄; 翁启勇

doi:10.19303/j.issn.1008-0384.2020.02.015

基于物联网的害虫智能监测系统设计与实现

doi: 10.19303/j.issn.1008-0384.2020.02.015

邱荣洲^{1, 2,},
赵健³,
池美香^{1, 2},
梁勇¹,
陈世雄¹,
翁启勇^{1, 2, ,}

1.
福建省农业科学院植物保护研究所，福建　福州　350013
2.
福建省作物有害生物监测与治理重点实验室，福建　福州　350003
3.
福建省农业科学院数字农业研究所，福建　福州　350003

基金项目: 中央引导地方科技发展专项（2017L3007）；福建省农业科学院科研项目（A2017-28）；福建省财政专项—福建省农业科学院科技创新团队建设项目（STIT2018-1-8）

详细信息

作者简介:
邱荣洲（1979−），男，副研究员，主要从事数字植保技术研究（E-mail：qiurz@faas.cn）

通讯作者:
翁启勇（1962−），男，硕士，研究员，研究方向：有害生物综合防治与农业信息学（E-mail：wengqy@faas.cn）

中图分类号: S 431.9
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- 被引次数: 0
出版历程
- 收稿日期: 2019-10-09
- 修回日期: 2019-12-26
- 刊出日期: 2020-02-01

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

QIU Rong-zhou^{1, 2
,},
ZHAO Jian³,
CHI Mei-xiang^{1, 2},
LIANG Yong¹,
CHEN Shi-xiong¹,
WENG Qi-yong^{1, 2
, ,}

1.
Institute of Plant Protection, Fujian Academy of Agriculture Sciences, Fuzhou, Fujian　350013, China
2.
Fujian Key Laboratory for Monitoring and Integrated Management of Crop Pests, Fuzhou, Fujian　350003, China
3.
Institute of Digital Agriculture, Fujian Academy of Agricultural Sciences, Fuzhou, Fujian　350003, China

摘要

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。
- 物联网 /
- 害虫监测 /
- 数字植保 /
- 自动计数
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.
- Internet of things /
- pest monitoring /
- digital plant protection /
- automatic counting

HTML全文

图 1 系统功能结构

Figure 1. Design of system functions

下载: 全尺寸图片幻灯片

图 2 害虫智能监测装置示意图与实物图

注：1：太阳能供电组件；2：数据传输单元（DTU）；3：高压电网；4：性信息素；5：害虫诱杀器；6：智能计数器；7：温湿度传感器；8：水泥墩。

Figure 2. Schematics and photos of intelligent pests monitoring system

Note: 1. solar power supply module; 2. data transmission unit（DTU）; 3. high voltage power grid; 4. sex pheromone; 5. trap and kill device for pest; 6. intelligent counter; 7. temperature and humidity sensor; 8. cement square pier.

下载: 全尺寸图片幻灯片

图 3 系统层次结构

Figure 3. Levels in monitoring system

下载: 全尺寸图片幻灯片

图 4 软件测试界面

注：a, 时数据列表；b, 时折线图；c, 实时数据与设备管理界面（手机APP）；d, 近1月虫量和24小时虫量界面（数字植保微信公众号）。

Figure 4. Interface for software testing

Note: a, List of hours; b, line chart of hour; c, real-time data and interface of device administration（mobile phone APP）;d, interface of the pest number in 1 month and interface of the pest number in 24 hours（digital plant protection of WeChat official）.

下载: 全尺寸图片幻灯片

图 5 害虫智能监测系统自动计数与人工计数、夜蛾诱捕对照的对比结果

注：a, 厦门同安试验点；b, 三明尤溪试验点。

Figure 5. Comparison on daily pest counts obtained by intelligent pests monitoring system, manual observation, and pheromone trapping

Note: a, Vegetable base of Tong'an, Xiamen; b, vegetable base of Youxi, Sanming.

下载: 全尺寸图片幻灯片

表 1 同安、尤溪试验点的计数正确率、诱捕效果比较

Table 1. Pest counting accuracy and trapping effects on tests at Tong'an and Youxi sites

试验点 Experimental sites	日期 Date（M/D）	天数 Days/d	自动计数总虫量 Machine counting	人工计数总虫量 Labor counting	对照总虫量 Pheromone traps counting	平均计数正确率 Average of counting accuracy/%	平均诱捕效果 Average of trap effect/%
厦门同安（诱虫量大） Tong'an (large number of trapping)	05-18—05-31	14	1 551	1 497	656	93.52	276.14
三明尤溪（诱虫量小） Youxi (small number of trapping)	05-25—06-07	14	64	56	50	81.59	162.60

下载: 导出CSV

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