Submitted to: National Cotton Council Beltwide Cotton Conference
Publication Type: Abstract Only
Publication Acceptance Date: 10/14/2021
Publication Date: 1/6/2022
Citation: Pelletier, M.G., Wanjura, J.D., Holt, G.A. 2022. Black plastic classifier development for plastic inspection and detection system on gin stand feeder. National Cotton Council Beltwide Cotton Conference. p. 325.
Technical Abstract: The most cost effective machine-vision systems are based on low-cost color cameras. The traditional machine-learning classifiers in use rely predominantly on color differences for detection of plastic. While this works for the vast majority of plastics brought into the cotton gins; it still leaves about 10-20% of the plastic contamination undetected as the colors are too close to normal cotton colors to allow for detection without significant risk of false-positives. For in-field plastics, the number of hard to detect plastics colors is greater due to wider range of colors presented by background as modified by the variation in natural lighting, where color temperature of the sky-light can vary from 3000k to over 10,000k, hence a primary research interest, for removal of plastic contamination from field and in cotton gins, is the identification of colored plastics where the plastic color is very similar to the normal background colors. This research development effort seeks to develop alternative classification methods, such as the use of cascaded traditional machine-learning algorithms, and as pre-processing sections for advanced deep-learning methods.