Plastic contamination in marketable cotton bales, predominantly from plastic wraps used in John Deere round module harvesters, poses a significant challenge to the U.S. cotton industry. Despite rigorous manual efforts, the intrusion of plastic into the cotton gin’s processing system persists. We have developed a machine-vision detection and removal system aimed at mitigating this problem. This system employs inexpensive color cameras to detect plastic on the gin-stand feeder apron and subsequently removes it, reducing contamination. This system, built around low-cost ARM computers running Linux, comprises 30–50 machines and requires considerable effort to calibrate and tune. Moreover, its operation represents a technological challenge to typical cotton gin workers. This research presents a solution to this calibration operational hurdle by introducing an auto-calibration algorithm that has potential to simplify the system into a plug-and-play device. The auto-calibration system is designed to dynamically track the cotton color and utilizes frequency statistics to avoid plastic images that could compromise the system’s performance if incorporated into the auto-calibration process. We detail the design of the auto-calibration algorithm, which is expected to significantly reduce the setup overhead and facilitate the system’s continuous use. This innovation minimizes the need for skilled personnel and, therefore, is expected to expedite the system’s adoption across the cotton ginning industry.

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