English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Zendy Plus

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Zendy Tools

Zendy Open

The boll weevil (Anthonomus grandis L.) is a serious pest that primarilyfeeds on cotton plants. In places like Lower Rio Grande Valley of Texas, due tosub-tropical climatic conditions, cotton plants can grow year-round andtherefore the left-over seeds from the previous season during harvest cancontinue to grow in the middle of rotation crops like corn (Zea mays L.) andsorghum (Sorghum bicolor L.). These feral or volunteer cotton (VC) plants whenreach the pinhead squaring phase (5-6 leaf stage) can act as hosts for the bollweevil pest. The Texas Boll Weevil Eradication Program (TBWEP) employs peopleto locate and eliminate VC plants growing by the side of roads or fields withrotation crops but the ones growing in the middle of fields remain undetected.In this paper, we demonstrate the application of computer vision (CV) algorithmbased on You Only Look Once version 5 (YOLOv5) for detecting VC plants growingin the middle of corn fields at three different growth stages (V3, V6, and VT)using unmanned aircraft systems (UAS) remote sensing imagery. All the fourvariants of YOLOv5 (s, m, l, and x) were used and their performances werecompared based on classification accuracy, mean average precision (mAP), andF1-score. It was found that YOLOv5s could detect VC plants with a maximumclassification accuracy of 98% and mAP of 96.3 % at the V6 stage of corn whileYOLOv5s and YOLOv5m resulted in the lowest classification accuracy of 85% andYOLOv5m and YOLOv5l had the least mAP of 86.5% at the VT stage on images ofsize 416 x 416 pixels. The developed CV algorithm has the potential toeffectively detect and locate VC plants growing in the middle of corn fields aswell as expedite the management aspects of TBWEP.

Assessing The Performance of YOLOv5 Algorithm for Detecting Volunteer Cotton Plants in Corn Fields at Three Different Growth Stages