Next Article in Journal
Development and Evaluation of a Closed-Loop Control System for Automation of a Mechanical Wild Blueberry Harvester’s Picking Reel
Next Article in Special Issue
Mapping and Estimating Weeds in Cotton Using Unmanned Aerial Systems-Borne Imagery
Previous Article in Journal
Effects of Solvent and pH on Stingless Bee Propolis in Ultrasound-Assisted Extraction
Previous Article in Special Issue
A Cotton Module Feeder Plastic Contamination Inspection System
Technical Note

A Plastic Contamination Image Dataset for Deep Learning Model Development and Training

Agricultural Research Services, United States Department of Agriculture, Lubbock, TX 79404, USA
*
Author to whom correspondence should be addressed.
Received: 17 April 2020 / Revised: 13 May 2020 / Accepted: 19 May 2020 / Published: 22 May 2020
(This article belongs to the Special Issue Feature Papers in Cotton Automation, Machine Vision and Robotics)
The removal of plastic contamination in cotton lint is an issue of top priority for the U.S. cotton industry. One of the main sources of plastic contamination appearing in marketable cotton bales is plastic used to wrap cotton modules on cotton harvesters. To help mitigate plastic contamination at the gin, automatic inspection systems are needed to detect and control removal systems. Due to significant cost constraints in the U.S. cotton ginning industry, the use of low-cost color cameras for detection of plastic contamination has been successfully adopted. However, some plastics of similar color to background are difficult to detect when utilizing traditional machine learning algorithms. Hence, current detection/removal system designs are not able to remove all plastics and there is still a need for better detection methods. Recent advances in deep learning convolutional neural networks (CNNs) show promise for enabling the use of low-cost color cameras for detection of objects of interest when placed against a background of similar color. They do this by mimicking the human visual detection system, focusing on differences in texture rather than color as the primary detection paradigm. The key to leveraging the CNNs is the development of extensive image datasets required for training. One of the impediments to this methodology is the need for large image datasets where each image must be annotated with bounding boxes that surround each object of interest. As this requirement is labor-intensive, there is significant value in these image datasets. This report details the included image dataset as well as the system design used to collect the images. For acquisition of the image dataset, a prototype detection system was developed and deployed into a commercial cotton gin where images were collected for the duration of the 2018–2019 ginning season. A discussion of the observational impact that the system had on reduction of plastic contamination at the commercial gin, utilizing traditional color-based machine learning algorithms, is also included. View Full-Text
Keywords: machine vision; plastic contamination; cotton; automated inspection machine vision; plastic contamination; cotton; automated inspection
Show Figures

Figure 1

MDPI and ACS Style

Pelletier, M.G.; Holt, G.A.; Wanjura, J.D. A Plastic Contamination Image Dataset for Deep Learning Model Development and Training. AgriEngineering 2020, 2, 317-321. https://0-doi-org.brum.beds.ac.uk/10.3390/agriengineering2020021

AMA Style

Pelletier MG, Holt GA, Wanjura JD. A Plastic Contamination Image Dataset for Deep Learning Model Development and Training. AgriEngineering. 2020; 2(2):317-321. https://0-doi-org.brum.beds.ac.uk/10.3390/agriengineering2020021

Chicago/Turabian Style

Pelletier, Mathew G., Greg A. Holt, and John D. Wanjura 2020. "A Plastic Contamination Image Dataset for Deep Learning Model Development and Training" AgriEngineering 2, no. 2: 317-321. https://0-doi-org.brum.beds.ac.uk/10.3390/agriengineering2020021

Find Other Styles

Article Access Map by Country/Region

1
Back to TopTop