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

Mathew G Pelletier,Greg A Holt,John D Wanjura

doi:10.3390/agriengineering2020021

Mathew G Pelletier, Greg A Holt + Show 1 more

Open Access

https://doi.org/10.3390/agriengineering2020021

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Abstract

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.

Highlights

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 at the U.S Department of Agriculture’s classing office is from the plastic used to wrap cotton modules produced by the new John Deere round module harvesters
Plastic contamination in cotton is thought to be a major contributor to the loss of a US$0.02/kg premium that U.S cotton used to obtain on the international market due to its reputation as one of the cleanest cottons in the world

Summary

Introduction

The removal of plastic contamination in cotton lint is an issue of top priority for the U.S cotton industry. Plastic contamination in cotton is thought to be a major contributor to the loss of a US$0.02/kg premium that U.S cotton used to obtain on the international market due to its reputation as one of the cleanest cottons in the world. Current data show that U.S cotton is trading at a US$0.01/kg discount relative to the market, with a total loss of US$0.034/kg with respect to market conditions prior to the wide-spread adoption of plastic-wrapped. The cost of this loss to U.S producers is in excess of US$750 million annually [1]. In order to help address this loss and mitigate plastic contamination at the cotton gin, inspecticoonttosynsmtemodsualeres. TThhaatt wwiillll eennssuurree tthhee ddeeeepp lleeaarrnniinngg aallggoorriitthhmmss ssiimmppllyy ddoo nnoott uuttiilliizzee ccoolloorr iinn tthhee bbaacckk--pprrooppaaggaattiioonn ttrraaiinniinngg ooff tthhee CCNNNN’’ss ccllaassssiififieerrss

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