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

Pappu Kumar Yadav (), J. Alex Thomasson, Robert Hardin, Stephen W. Searcy, Ulisses Braga-Neto, Sorin C. Popescu, Roberto Rodriguez, Daniel E. Martin, Juan Enciso, Karem Meza and Emma L. White
Additional contact information
Pappu Kumar Yadav: Department of Biological & Agricultural Engineering, Texas A&M University, College Station, TX 77843, USA
J. Alex Thomasson: Department of Agricultural & Biological Engineering, Mississippi State University, Starkville, MS 39762, USA
Robert Hardin: Department of Biological & Agricultural Engineering, Texas A&M University, College Station, TX 77843, USA
Stephen W. Searcy: Department of Biological & Agricultural Engineering, Texas A&M University, College Station, TX 77843, USA
Ulisses Braga-Neto: Department of Electrical & Computer Engineering, Texas A&M University, College Station, TX 77843, USA
Sorin C. Popescu: Department of Ecology & Conservation Biology, Texas A&M University, College Station, TX 77843, USA
Roberto Rodriguez: Spatial Data Analysis and Visualization Laboratory, University of Hawaii at Hilo, Hilo, HI 96720, USA
Daniel E. Martin: Aerial Application Technology Research, U.S.D.A. Agriculture Research Service, College Station, TX 77845, USA
Juan Enciso: Department of Biological & Agricultural Engineering, Texas A&M University, College Station, TX 77843, USA
Karem Meza: Department of Civil & Environmental Engineering, Utah State University, Logan, UT 84322, USA
Emma L. White: Department of Biological & Agricultural Engineering, Texas A&M University, College Station, TX 77843, USA

Agriculture, 2023, vol. 13, issue 7, 1-22

Abstract: 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.

Keywords: plastic contamination; cotton field; YOLOv5; unmanned aircraft systems (UAS) (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2023
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