Vision-transformer, ViT, model validation image dataset

Authors: Pelletier, Mathew; Wanjura, John; Holt, Gregory

Submitted to: AgriEngineering
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 11/20/2024
Publication Date: 11/25/2024
Citation: Pelletier, M.G., Wanjura, J.D., Holt, G.A. 2024. Vision-transformer, ViT, model validation image dataset. AgriEngineering. 6(4). https://doi.org/10.3390/agriengineering6040254.
DOI: https://doi.org/10.3390/agriengineering6040254

Interpretive Summary: The U.S. cotton industry is highly concerned with removing plastic contamination from cotton lint. A major source of this contamination is the plastic used to wrap cotton modules produced by John Deere round module harvesters. A machine-vision detection and removal system has been developed to address this problem, using low-cost color cameras to detect plastic in the cotton stream and remove it. However, the system requires a lot of calibration and is difficult for cotton gin workers to operate due to its reliance on low-cost ARM computers running Linux. This research aims to make the system more user-friendly by adding an auto-calibration feature that can track cotton colors and avoid plastic images, reducing the need for skilled personnel to operate the system and making it easier for the cotton ginning industry to adopt. This image dataset was created to validate several Vision-Transformer, ViT, AI models that in combination provides the key enabling technology for the auto-calibration code.

Technical Abstract: 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 calibration and tuning, requiring extensive technical knowledge. This re-search 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 an image dataset that was used to validate the design 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 broader adoption of the plastic removal system in the cotton ginning industry.