  To control boll weevil (Anthonomus grandis L.) pest re-infestation in cotton
fields, the current practices of volunteer cotton (VC) (Gossypium hirsutum L.)
plant detection in fields of rotation crops like corn (Zea mays L.) and sorghum
(Sorghum bicolor L.) involve manual field scouting at the edges of fields. This
leads to many VC plants growing in the middle of fields remain undetected that
continue to grow side by side along with corn and sorghum. When they reach
pinhead squaring stage (5-6 leaves), they can serve as hosts for the boll
weevil pests. Therefore, it is required to detect, locate and then precisely
spot-spray them with chemicals. In this paper, we present the application of
YOLOv5m on radiometrically and gamma-corrected low resolution (1.2 Megapixel)
multispectral imagery for detecting and locating VC plants growing in the
middle of tasseling (VT) growth stage of cornfield. Our results show that VC
plants can be detected with a mean average precision (mAP) of 79% and
classification accuracy of 78% on images of size 1207 x 923 pixels at an
average inference speed of nearly 47 frames per second (FPS) on NVIDIA Tesla
P100 GPU-16GB and 0.4 FPS on NVIDIA Jetson TX2 GPU. We also demonstrate the
application of a customized unmanned aircraft systems (UAS) for spot-spray
applications based on the developed computer vision (CV) algorithm and how it
can be used for near real-time detection and mitigation of VC plants growing in
corn fields for efficient management of the boll weevil pests.
