A practical guide to UAV-based weed identification in soybean: Comparing RGB and multispectral sensor performance

IF 4.8 Q1 AGRICULTURE, MULTIDISCIPLINARY
Kelvin Betitame , Cannayen Igathinathane , Kirk Howatt , Joseph Mettler , Cengiz Koparan , Xin Sun
{"title":"A practical guide to UAV-based weed identification in soybean: Comparing RGB and multispectral sensor performance","authors":"Kelvin Betitame ,&nbsp;Cannayen Igathinathane ,&nbsp;Kirk Howatt ,&nbsp;Joseph Mettler ,&nbsp;Cengiz Koparan ,&nbsp;Xin Sun","doi":"10.1016/j.jafr.2025.101784","DOIUrl":null,"url":null,"abstract":"<div><div>Precision agriculture relies heavily on accurate, efficient, and economical methods to distinguish between crops and weeds of various types. The advancement of unmanned aerial vehicle (UAV) technologies provides practical approaches for generating land-cover maps that are essential for monitoring and managing crop fields affected by various weeds. Although the overall cost of scouting crop fields with UAVs may be low and practical, it varies depending on the sensors used; and the existing studies have mainly focused on weed detection methods but not compared the sensors' performance. Therefore, to address this knowledge gap, this research aims to compare a UAV-mounted visual Red-Green-Blue (RGB) sensor and a multispectral sensor in differentiating between crops and weeds in soybean fields, with a particular focus on broadleaf and grass weeds. In this research, a field study was conducted using a support vector machine classification algorithm and object-based image analysis in ArcGIS Pro to examine the impact of sensor choice on weed type differentiation. The analysis with ground truths highlights nuanced discrepancies between the sensors, namely (i) DJI Phantom 4 Pro (RGBd), and (ii) DJI Phantom 4 Multispectral. Overall, with the RGB sensor, an accuracy of 93.8 % was achieved in identifying the land cover types, and the multispectral sensor also had an accuracy of 93.4 % in discriminating the various land cover types. These results show that both sensor's performances were similar, but the less expensive RGB sensor may be better suited precision agriculture at all scales.</div></div>","PeriodicalId":34393,"journal":{"name":"Journal of Agriculture and Food Research","volume":"20 ","pages":"Article 101784"},"PeriodicalIF":4.8000,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Agriculture and Food Research","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2666154325001553","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"AGRICULTURE, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0

Abstract

Precision agriculture relies heavily on accurate, efficient, and economical methods to distinguish between crops and weeds of various types. The advancement of unmanned aerial vehicle (UAV) technologies provides practical approaches for generating land-cover maps that are essential for monitoring and managing crop fields affected by various weeds. Although the overall cost of scouting crop fields with UAVs may be low and practical, it varies depending on the sensors used; and the existing studies have mainly focused on weed detection methods but not compared the sensors' performance. Therefore, to address this knowledge gap, this research aims to compare a UAV-mounted visual Red-Green-Blue (RGB) sensor and a multispectral sensor in differentiating between crops and weeds in soybean fields, with a particular focus on broadleaf and grass weeds. In this research, a field study was conducted using a support vector machine classification algorithm and object-based image analysis in ArcGIS Pro to examine the impact of sensor choice on weed type differentiation. The analysis with ground truths highlights nuanced discrepancies between the sensors, namely (i) DJI Phantom 4 Pro (RGBd), and (ii) DJI Phantom 4 Multispectral. Overall, with the RGB sensor, an accuracy of 93.8 % was achieved in identifying the land cover types, and the multispectral sensor also had an accuracy of 93.4 % in discriminating the various land cover types. These results show that both sensor's performances were similar, but the less expensive RGB sensor may be better suited precision agriculture at all scales.

Abstract Image

求助全文
约1分钟内获得全文 求助全文
来源期刊
CiteScore
5.40
自引率
2.60%
发文量
193
审稿时长
69 days
×
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
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
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 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信