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

Plastic shopping bags are often discarded as litter and can be carried away from roadsides and become tangled on cotton plants in farm fields. This rubbish plastic can end up in the cotton at the gin if not removed before harvest. These bags may not only cause problems in the ginning process but mig...

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Bibliographic Details
Published in:Agriculture (Basel) 2023-07, Vol.13 (7), p.1365
Main Authors: Yadav, Pappu Kumar, Thomasson, J. Alex, Hardin, Robert, Searcy, Stephen W., Braga-Neto, Ulisses, Popescu, Sorin C., Rodriguez III, Roberto, Martin, Daniel E, Enciso, Juan, Meza, Karem, White, Emma L.
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Cameras
Contaminants
Cotton
Cotton fibers
cotton field
Cotton ginning
Cotton gins
Cotton industry
Deep learning
Drone aircraft
Fibers
Harvesting
Image acquisition
Mitigation
plastic contamination
Plastics
Probability
Roadsides
Shopping
Software
Supply chains
Textile industry
Unmanned aircraft
unmanned aircraft systems (UAS)
YOLOv5
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Summary:Plastic shopping bags are often discarded as litter and can be carried away from roadsides and become tangled on cotton plants in farm fields. This rubbish plastic can end up in the cotton at the gin if not removed before harvest. These bags may not only cause problems in the ginning process but might also become embedded in cotton fibers, reducing the quality and marketable value. Therefore, detecting, locating, and removing the bags before the cotton is harvested is required. Manually detecting and locating these bags in cotton fields is a tedious, time-consuming, and costly process. To solve this, this paper shows the application of YOLOv5 to detect white and brown colored plastic bags tangled at three different heights in cotton plants (bottom, middle, top) using Unmanned Aircraft Systems (UAS)-acquired Red, Green, Blue (RGB) images. It was found that an average white and brown bag could be detected at 92.35% and 77.87% accuracies and a mean average precision (mAP) of 87.68%. Similarly, the trained YOLOv5 model, on average, could detect 94.25% of the top, 49.58% of the middle, and only 5% of the bottom bags. It was also found that both the color of the bags (p < 0.001) and their height on cotton plants (p < 0.0001) had a significant effect on detection accuracy. The findings reported in this paper can help in the autonomous detection of plastic contaminants in cotton fields and potentially speed up the mitigation efforts, thereby reducing the amount of contaminants in cotton gins.
ISSN:2077-0472
2077-0472
DOI:10.3390/agriculture13071365