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Plastic shopping bags that get carried away from the side of roads andtangled on cotton plants can end up at cotton gins if not removed before theharvest. Such bags may not only cause problem in the ginning process but mightalso get embodied in cotton fibers reducing its quality and marketable value.Therefore, it is required to detect, locate, and remove the bags before cottonis harvested. Manually detecting and locating these bags in cotton fields islabor intensive, time-consuming and a costly process. To solve thesechallenges, we present application of four variants of YOLOv5 (YOLOv5s,YOLOv5m, YOLOv5l and YOLOv5x) for detecting plastic shopping bags usingUnmanned Aircraft Systems (UAS)-acquired RGB (Red, Green, and Blue) images. Wealso show fixed effect model tests of color of plastic bags as well asYOLOv5-variant on average precision (AP), mean average precision (mAP@50) andaccuracy. In addition, we also demonstrate the effect of height of plastic bagson the detection accuracy. It was found that color of bags had significanteffect (p < 0.001) on accuracy across all the four variants while it did notshow 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 anysignificant 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) ofbrown 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) onoverall detection accuracy. The findings reported in this paper can be usefulin speeding up removal of plastic bags from cotton fields before harvest andthereby reducing the amount of contaminants that end up at cotton gins.

Plastic Contaminant Detection in Aerial Imagery of Cotton Fields with Deep Learning