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

Abstract

Plastic shopping bags are often discarded as litter and can get carried away from roadsides and tangled on cotton plants in farm fields. This rubbish plastic can end up in cotton at the gin if not removed before the harvest. Such bags may not only cause problems 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 consider the effect of the height of plastic bags on plants in terms of 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. Using the desirability function, we found YOLOv5m to be the most desirable variant among all the four with desirability value of nearly 95% based on mAP@50, accuracies of white and brown bags, and inference speed. 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.

Keywords: Plastic contamination, Cotton field, YOLOv5, Unmanned Aircraft Systems (UAS), Computer Vision (CV), Desirability function