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To control boll weevil (Anthonomus grandis L.) pest re-infestation in cottonfields, 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. Thisleads to many VC plants growing in the middle of fields remain undetected thatcontinue to grow side by side along with corn and sorghum. When they reachpinhead squaring stage (5-6 leaves), they can serve as hosts for the bollweevil pests. Therefore, it is required to detect, locate and then preciselyspot-spray them with chemicals. In this paper, we present the application ofYOLOv5m on radiometrically and gamma-corrected low resolution (1.2 Megapixel)multispectral imagery for detecting and locating VC plants growing in themiddle of tasseling (VT) growth stage of cornfield. Our results show that VCplants can be detected with a mean average precision (mAP) of 79% andclassification accuracy of 78% on images of size 1207 x 923 pixels at anaverage inference speed of nearly 47 frames per second (FPS) on NVIDIA TeslaP100 GPU-16GB and 0.4 FPS on NVIDIA Jetson TX2 GPU. We also demonstrate theapplication of a customized unmanned aircraft systems (UAS) for spot-sprayapplications based on the developed computer vision (CV) algorithm and how itcan be used for near real-time detection and mitigation of VC plants growing incorn fields for efficient management of the boll weevil pests.

Computer Vision for Volunteer Cotton Detection in a Corn Field with UAS Remote Sensing Imagery and Spot Spray Applications