Fruit fly automatic detection and monitoring techniques: A review

IF 5.7 Q1 AGRICULTURAL ENGINEERING Smart agricultural technology Pub Date : 2023-10-01 DOI:10.1016/j.atech.2023.100294
Florence Lello , Mussa Dida , Mbazingwa Mkiramweni , Joseph Matiko , Roseline Akol , Mary Nsabagwa , Andrew Katumba
{"title":"Fruit fly automatic detection and monitoring techniques: A review","authors":"Florence Lello ,&nbsp;Mussa Dida ,&nbsp;Mbazingwa Mkiramweni ,&nbsp;Joseph Matiko ,&nbsp;Roseline Akol ,&nbsp;Mary Nsabagwa ,&nbsp;Andrew Katumba","doi":"10.1016/j.atech.2023.100294","DOIUrl":null,"url":null,"abstract":"<div><p>Fruit flies affect the production and market of fresh fruits and vegetables worldwide. To minimize their effects, integrated pest management (IPM) strategies are needed. However, the adopted IPM strategies involve human operators who manually monitor and evaluate insect pests and their effects, respectively. The manual methods involved are tedious, labor-intensive, and prone to errors. To avoid the drawbacks, monitoring processes can be made automatic, which involves the detection of the flies without human operator intervention. To achieve automatic detection and monitoring, insect traps are equipped with electronic sensors (i.e., optical, acoustic, or image) for accurate and efficient monitoring. The traps are further linked together over a communication network, allowing the pests to be monitored remotely without requiring frequent field visits, which leads to smart traps. In this work, we summarize automatic techniques that are used to monitor pests in fruit production (such as mangoes, apples, and olives).</p></div>","PeriodicalId":74813,"journal":{"name":"Smart agricultural technology","volume":"5 ","pages":"Article 100294"},"PeriodicalIF":5.7000,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Smart agricultural technology","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2772375523001235","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"AGRICULTURAL ENGINEERING","Score":null,"Total":0}
引用次数: 1

Abstract

Fruit flies affect the production and market of fresh fruits and vegetables worldwide. To minimize their effects, integrated pest management (IPM) strategies are needed. However, the adopted IPM strategies involve human operators who manually monitor and evaluate insect pests and their effects, respectively. The manual methods involved are tedious, labor-intensive, and prone to errors. To avoid the drawbacks, monitoring processes can be made automatic, which involves the detection of the flies without human operator intervention. To achieve automatic detection and monitoring, insect traps are equipped with electronic sensors (i.e., optical, acoustic, or image) for accurate and efficient monitoring. The traps are further linked together over a communication network, allowing the pests to be monitored remotely without requiring frequent field visits, which leads to smart traps. In this work, we summarize automatic techniques that are used to monitor pests in fruit production (such as mangoes, apples, and olives).

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
果蝇自动检测与监测技术综述
果蝇影响着全球新鲜水果和蔬菜的生产和市场。为了尽量减少其影响,需要采取综合病虫害管理战略。然而,采用的IPM策略涉及人工操作人员,他们分别手动监测和评估害虫及其影响。所涉及的手工方法繁琐、费力,而且容易出错。为了避免这些缺点,监测过程可以自动进行,这涉及到在没有人工操作人员干预的情况下检测苍蝇。为了实现自动检测和监测,捕虫器配备了电子传感器(即光学、声学或图像),以进行准确和有效的监测。这些陷阱通过一个通信网络进一步连接在一起,允许远程监测害虫,而不需要频繁的实地考察,这导致了智能陷阱。在这项工作中,我们总结了用于监测水果生产中有害生物的自动技术(如芒果、苹果和橄榄)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
CiteScore
4.20
自引率
0.00%
发文量
0
期刊最新文献
Animating the transition: How agriculture 5.0 revitalises agroecological principles Multi branch model based on cross scale feature fusion for wheat seedling variety recognition CANBUS to drawbar load estimation: Mapping real-world tractor loads for mission profiling The nexus of big data, internet of things-enabled agro-technologies, and farm performance Enhancing weed detection in strawberry plasticulture: Evaluating stable diffusion models for synthetic image generation of Geranium carolinianum
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1