  Plastic shopping bags that get carried away from the side of roads and
tangled on cotton plants can end up at cotton gins if not removed before the
harvest. Such bags may not only cause problem in the ginning process but might
also get embodied in cotton fibers reducing its quality and marketable value.
Therefore, it is required to detect, locate, and remove the bags before cotton
is harvested. Manually detecting and locating these bags in cotton fields is
labor intensive, time-consuming and a costly process. To solve these
challenges, we present application of four variants of YOLOv5 (YOLOv5s,
YOLOv5m, YOLOv5l and YOLOv5x) for detecting plastic shopping bags using
Unmanned Aircraft Systems (UAS)-acquired RGB (Red, Green, and Blue) images. We
also show fixed effect model tests of color of plastic bags as well as
YOLOv5-variant on average precision (AP), mean average precision (mAP@50) and
accuracy. In addition, we also demonstrate the effect of height of plastic bags
on the detection accuracy. It was found that color of bags had significant
effect (p < 0.001) on accuracy across all the four variants while it did not
show any significant effect on the AP with YOLOv5m (p = 0.10) and YOLOv5x (p =
0.35) at 95% confidence level. Similarly, YOLOv5-variant did not show any
significant effect on the AP (p = 0.11) and accuracy (p = 0.73) of white bags,
but it had significant effects on the AP (p = 0.03) and accuracy (p = 0.02) of
brown bags including on the mAP@50 (p = 0.01) and inference speed (p < 0.0001).
Additionally, height of plastic bags had significant effect (p < 0.0001) on
overall detection accuracy. The findings reported in this paper can be useful
in speeding up removal of plastic bags from cotton fields before harvest and
thereby reducing the amount of contaminants that end up at cotton gins.
