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Assessing the performance of YOLOv5 algorithm for detecting volunteer cotton plants in corn fields at three different growth stages

引用

Artificial Intelligence in Agriculture 2022年第1期6卷 292-303页

作者： pappu kumar yadav.j.alex thomasson Stephen W.Searcy Robert G.Hardin Ulisses Braga-Neto Sorin C.Popescu Daniel E.Martin Roberto Rodriguez Karem Meza juan Enciso jorge Solórzano Diaz Tianyi Wang Department of Biological&Agricultural Engineering Texas A&M UniversityCollege StationTXUnited States of America Department of Agricultural&Biological Engineering Mississippi State UniversityMississippi StateMSUnited States of America Department of Electrical&Computer Engineering Texas A&M UniversityCollege StationTXUnited States of America Department of Ecology&Conservation Biology Texas A&M UniversityCollege StationTXUnited States of America Aerial Application Technology Research U.S.D.A.Agriculture Research ServiceCollege StationTXUnited States of America U.S.D.A.-APHIS PPQ S&T Mission LabEdinburgTXUnited States of America Department of Civil&Environmental Engineering Utah State UniversityLoganUTUnited States of America Texas A&M AgriLife Research&Extension Center WeslacoTXUnited States of America College of Engineering China Agricultural UniversityBeijingChina

The feral or volunteer cotton(VC)plants when reach the pinhead squaring phase(5–6 leaf stage)can act as hosts for the boll weevil(Anthonomus grandis L.)*** Texas Boll Weevil Eradication Program(TBWEP)employs people to locate and eliminate VC plants growing by the side of roads or fields with rotation crops but the ones growing in the middle of fields remain *** this paper,we demonstrate the application of computer vision(CV)algorithm based on You Only Look Once version 5(YOLOv5)for detecting VC plants growing in the middle of corn fields at three different growth stages(V3,V6 and VT)using unmanned aircraft systems(UAS)remote sensing *** the four variants of YOLOv5(s,m,l,and x)were used and their performances were compared based on classification accuracy,mean average precision(mAP)and *** was found that YOLOv5s could detect VC plants with maximum classification accuracy of 98%and mAP of 96.3%at V6 stage of corn while YOLOv5s and YOLOv5m resulted in the lowest classification accuracy of 85%and YOLOv5m and YOLOv5l had the least mAP of 86.5%at VT stage on images of size 416×416 *** developed CV algorithm has the potential to effectively detect and locate VC plants growing in the middle of corn fields as well as expedite the management aspects of TBWEP.

关键词： Boll weevil Volunteer cotton plant Computer vision YOLOv5 Unmanned aircraft systems(UAS) Remote sensing

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