Vision-Transformer Model Validation Image Dataset - Mathew G. Pelletier; John D. Wanjura; Greg A. Holt (AgriEngineering)
The removal of plastic contamination from cotton lint is a critical issue for the U.S. cotton industry. One primary source of this contamination is the plastic wrap used on cotton modules by John Deere round module harvesters. Despite rigorous efforts by cotton ginning personnel to eliminate plastic during module unwrapping, fragments still enter the gin’s processing system. To address this, we developed a machine-vision detection and removal system using low-cost color cameras to identify and expel plastic from the gin-stand feeder apron, preventing contamination. However, the system, comprising 30–50 ARM computers running Linux, poses significant challenges in terms of calibration and tuning, requiring extensive technical knowledge. This research aims to transform the system into a plug-and-play appliance by incorporating an auto-calibration algorithm that dynamically tracks cotton colors and excludes plastic images to maintain calibration integrity. We present the image dataset that was used to validate the design, consisting of several key AI Vision-Transformer image classifiers that form the heart of the auto-calibration algorithm, which is expected to reduce setup and operational overhead significantly. The auto-calibration feature will minimize the need for skilled personnel, facilitating the broader adoption of the plastic removal system in the cotton ginning industry.