Cotton Gin Stand Machine-Vision Inspection and Removal System for Plastic Contamination: Software Design

Pelletier, Mathew G.; Holt, Greg A.; Wanjura, John D.

doi:10.3390/agriengineering3030033

Cited by 10 publications

(8 citation statements)

References 4 publications

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“…Machine vision has broad application prospects in different fields: graph optimization processing [1], sensors [2], sound source reconstruction [3], visual prediction [4], and pollution monitoring [5]. In view of a series of problems existing in the process of maize species analysis based on machine vision theory, the authenticity analysis method was introduced to obtain an optimized machine vision analysis model [6].…”

Section: Introductionmentioning

confidence: 99%

Design and Application of Art Education Communication Platform Based on Machine Vision

Chen

Tang

et al. 2022

Security and Communication Networks

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There are some problems in the design and application of the art education exchange platform. In order to further analyze the relevant content of the art education exchange, based on machine vision theory, model calibration, and coordinate change are adopted to modify the original model, so as to obtain the optimized model. This model can be used to analyze the design and application of art education platform. Relevant studies show that, according to the coordinate parameter curve, the parameter α plays a promoting role on the coordinate value, while the parameter β plays a promoting role on the coordinate value first and then plays an inhibiting role. The parameter γ also promotes and inhibits coordinate values. The curve corresponding to parameter M1 shows a gradual decline, and the curve of parameter M2 has little influence on coordinate data. The curve corresponding to parameter M3 also shows a linear trend of change. Through analysis, it can be seen that M3 has the greatest influence on coordinate data. By using machine vision model to design and analyze the communication platform of art education, the changing trend of different indicators can be obtained. It can be seen from the analysis that both language and vocal music have obvious stability in the calculation process, which is an inherent attribute of communication platform. Finally, the accuracy of the optimization model is verified by analyzing and predicting the data. This research can provide theoretical support for the application of machine vision model in other fields.

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Section: Introductionmentioning

confidence: 99%

Design and Application of Art Education Communication Platform Based on Machine Vision

Chen

Tang

et al. 2022

Security and Communication Networks

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“…Furthermore, other research works focused on mitigating plastic contamination at the cotton gin include the development of a machine vision-based plastic detection-ejection system by researchers at the USDA-ARS Gin Lab, Lubbock (in collaboration with Bratney Companies, Des Moines, IA, USA, a leading company in industrial optical sorting and cleaning operation) which detects incoming plastic materials at the gin-stand apron and puffs them out with air produced by a set of solenoid-controlled air-knives. This system has been tested with satisfactory performance in-house at the USDA and some commercial gins during multiple seasons [76]. The success of the tested prototype has led to the production of a commercial model marketed as Visual Imaging Plastic Removal (VIPR™) by Lummus Ag Technology (Savanna, GA, USA) and Bratney Companies [77].…”

Section: Plastic Mitigation Efforts: Ginning Phasementioning

confidence: 99%

A Review of Plastic Contamination Challenges and Mitigation Efforts in Cotton and Textile Milling Industries

Adeleke¹

2022

Preprint

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Plastic contamination is a topical issue in the cotton and textile industries. These plastic contaminants come from diverse sources, including agricultural mulch films, improperly disposed plastic trash near cotton fields, and importantly, the plastic wraps used to cover cylindrical modules built by John Deere’s onboard module builder harvesters. When these different plastic materials end up in the cotton lint bales, the value of the bale to the textile mills plummets significantly. To that end, there has been a concerted effort by the two major industries by this issue to find lasting solutions to the menace posed to their profitability by plastic. In this review article, the subject of plastic contamination within these industries is first put into perspective. Thereafter, the cotton value chain is subdivided into different phases from pre-cultivation to textile mills. The root causes of plastic contamination in each of the phases are then analyzed, followed by discussions of the select solutions that have been developed or are being developed in response to the challenge by the industry and researchers. Finally, from the author’s perspective, brief projections for the future direction of plastic mitigation efforts within the cotton and textile industries are presented. This review is envisaged to serve as a useful reference for new hands starting their research career in cotton- and textile-related industries, as well as practicing professionals needing to keep abreast of developments in the industry.

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“…Despite diligent efforts by cotton ginning personnel to remove all plastic encountered during module unwrapping, plastic still finds a way into the cotton gin's processing system. In order to help address this revenue-loss, engineers at the USDA-ARS Cotton Production and Processing Research Unit developed an automated robotic machine-vision-based detection-ejection system that was designed to rapidly identify and then remove these pieces of plastic from the cotton flow [89,90]. The location selected was on the gin-stand feeder apron, just prior to entering the gin stand, where the cotton is spread out to the thinnest stream in the ginning process.…”

Section: Ginningmentioning

confidence: 99%

Opportunities for Robotic Systems and Automation in Cotton Production

et al. 2021

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Automation continues to play a greater role in agricultural production with commercial systems now available for machine vision identification of weeds and other pests, autonomous weed control, and robotic harvesters for fruits and vegetables. The growing availability of autonomous machines in agriculture indicates that there are opportunities to increase automation in cotton production. This article considers how current and future advances in automation has, could, or will impact cotton production practices. The results are organized to follow the cotton production process from land preparation to planting to within season management through harvesting and ginning. For each step, current and potential opportunities to automate processes are discussed. Specific examples include advances in automated weed control and progress made in the use of robotic systems for cotton harvesting.

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Cotton Gin Stand Machine-Vision Inspection and Removal System for Plastic Contamination: Software Design

Cited by 10 publications

References 4 publications

Design and Application of Art Education Communication Platform Based on Machine Vision

Design and Application of Art Education Communication Platform Based on Machine Vision

A Review of Plastic Contamination Challenges and Mitigation Efforts in Cotton and Textile Milling Industries

Opportunities for Robotic Systems and Automation in Cotton Production

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