Vision-Transformer Model Validation Image Dataset
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...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-11-01
|
| Series: | AgriEngineering |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2624-7402/6/4/254 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1846106327171989504 |
|---|---|
| author | Mathew G. Pelletier John D. Wanjura Greg A. Holt |
| author_facet | Mathew G. Pelletier John D. Wanjura Greg A. Holt |
| author_sort | Mathew G. Pelletier |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-147058e9c2c64ee7bca2ffdb1329eade |
| institution | Kabale University |
| issn | 2624-7402 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | AgriEngineering |
| spelling | doaj-art-147058e9c2c64ee7bca2ffdb1329eade2024-12-27T14:03:41ZengMDPI AGAgriEngineering2624-74022024-11-01644476447910.3390/agriengineering6040254Vision-Transformer Model Validation Image DatasetMathew G. Pelletier0John D. Wanjura1Greg A. Holt2Lubbock Gin-Lab., Agricultural Research Services, Cotton Production, and Processing Research Unit, United States Department of Agriculture, Lubbock, TX 79403, USALubbock Gin-Lab., Agricultural Research Services, Cotton Production, and Processing Research Unit, United States Department of Agriculture, Lubbock, TX 79403, USALubbock Gin-Lab., Agricultural Research Services, Cotton Production, and Processing Research Unit, United States Department of Agriculture, Lubbock, TX 79403, USAThe 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.https://www.mdpi.com/2624-7402/6/4/254machine-visionplastic contaminationcottonautomated inspection |
| spellingShingle | Mathew G. Pelletier John D. Wanjura Greg A. Holt Vision-Transformer Model Validation Image Dataset AgriEngineering machine-vision plastic contamination cotton automated inspection |
| title | Vision-Transformer Model Validation Image Dataset |
| title_full | Vision-Transformer Model Validation Image Dataset |
| title_fullStr | Vision-Transformer Model Validation Image Dataset |
| title_full_unstemmed | Vision-Transformer Model Validation Image Dataset |
| title_short | Vision-Transformer Model Validation Image Dataset |
| title_sort | vision transformer model validation image dataset |
| topic | machine-vision plastic contamination cotton automated inspection |
| url | https://www.mdpi.com/2624-7402/6/4/254 |
| work_keys_str_mv | AT mathewgpelletier visiontransformermodelvalidationimagedataset AT johndwanjura visiontransformermodelvalidationimagedataset AT gregaholt visiontransformermodelvalidationimagedataset |