Assessing The Performance of YOLOv5 Algorithm for Detecting Volunteer Cotton Plants in Corn Fields at Three Different Growth Stages
The boll weevil (Anthonomus grandis L.) is a serious pest that primarily feeds on cotton plants. In places like Lower Rio Grande Valley of Texas, due to sub-tropical climatic conditions, cotton plants can grow year-round and therefore the left-over seeds from the previous season during harvest can c...
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Main Authors | , , , , , , , , , , , |
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Format | Journal Article |
Language | English |
Published |
31.07.2022
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Subjects | |
Online Access | Get full text |
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Summary: | The boll weevil (Anthonomus grandis L.) is a serious pest that primarily
feeds on cotton plants. In places like Lower Rio Grande Valley of Texas, due to
sub-tropical climatic conditions, cotton plants can grow year-round and
therefore the left-over seeds from the previous season during harvest can
continue to grow in the middle of rotation crops like corn (Zea mays L.) and
sorghum (Sorghum bicolor L.). These feral or volunteer cotton (VC) plants when
reach the pinhead squaring phase (5-6 leaf stage) can act as hosts for the boll
weevil pest. The 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 undetected.
In 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 imagery. All 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
F1-score. It was found that YOLOv5s could detect VC plants with a maximum
classification accuracy of 98% and mAP of 96.3 % at the 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 the VT stage on images of
size 416 x 416 pixels. The 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. |
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DOI: | 10.48550/arxiv.2208.00519 |