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

Author:

Yadav Pappu Kumar1,Thomasson J. Alex2ORCID,Hardin Robert1,Searcy Stephen W.1,Braga-Neto Ulisses3,Popescu Sorin C.4,Rodriguez III Roberto5ORCID,Martin Daniel E6ORCID,Enciso Juan1ORCID,Meza Karem7,White Emma L.1

Affiliation:

1. Department of Biological & Agricultural Engineering, Texas A&M University, College Station, TX 77843, USA

2. Department of Agricultural & Biological Engineering, Mississippi State University, Mississippi State, MS 39762, USA

3. Department of Electrical & Computer Engineering, Texas A&M University, College Station, TX 77843, USA

4. Department of Ecology & Conservation Biology, Texas A&M University, College Station, TX 77843, USA

5. Spatial Data Analysis and Visualization Laboratory, University of Hawaii at Hilo, Hilo, HI 96720, USA

6. Aerial Application Technology Research, U.S.D.A. Agriculture Research Service, College Station, TX 77845, USA

7. Department of Civil & Environmental Engineering, Utah State University, Logan, UT 84322, USA

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.

Publisher

MDPI AG

Subject

Plant Science,Agronomy and Crop Science,Food Science

Reference49 articles.

1. Robbins, R.F. (2023, July 08). New Extraneous Matter Code for Plastic Contaminants in Cotton Samples, Memphis, TN, USA, Available online: https://mymarketnews.ams.usda.gov/sites/default/files/resources/2018-08/New%20Extraneous%20Matter%20Code%20for%20Plastic%20Contaminants%20in%20Cotton%20Samples_0.pdf.

2. A Cotton Module Feeder Plastic Contamination Inspection System;Pelletier;AgriEngineering,2020

3. Whitelock, D., Pelletier, M., Thomasson, A., Buser, M., Xu, B., Delhom, C., and Hardin, R. (2021, June 04). Current University and USDA Lab Cotton Contamination Research. Available online: http://www.cotton.org/beltwide/proceedings/2005-2019/data/conferences/2018/papers/18518.pdf#page=1.

4. Development and use of an Attenuated Total Reflectance/Fourier Transform Infrared (ATR/FT-IR) spectral database to identify foreign matter in cotton;Himmelsbach;J. Agric. Food Chem.,2006

5. Wanjura, J., Pelletier, M., Ward, J., Hardin, B., and Barnes, E. (2020). Prevention of Plastic Contamination When Handling Cotton Modules, Cotton Incorporated.

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