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

2021 ◽  
Vol 3 (3) ◽  
pp. 494-518
Author(s):  
Mathew G. Pelletier ◽  
Greg A. Holt ◽  
John D. Wanjura

The removal of plastic contamination from cotton lint is an issue of top priority to the U.S. cotton industry. One of the main sources of plastic contamination showing up in marketable cotton bales is plastic used to wrap cotton modules produced by John Deere round module harvesters. 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. To help mitigate plastic contamination at the gin, a machine-vision detection and removal system was developed that utilizes low-cost color cameras to see plastic coming down the gin-stand feeder apron, which upon detection, blows plastic out of the cotton stream to prevent contamination. This paper presents the software design of this inspection and removal system. The system was tested throughout the entire 2019 cotton ginning season at two commercial cotton gins and at one gin in the 2018 ginning season. The focus of this report is to describe the software design and discuss relevant issues that influenced the design of the software.

2020 ◽  
Vol 2 (2) ◽  
pp. 280-293
Author(s):  
Mathew G. Pelletier ◽  
Greg A. Holt ◽  
John D. Wanjura

The removal of plastic contamination in cotton lint is an issue of top priority to the U.S. cotton industry. One of the main sources of plastic contamination showing up in marketable cotton bales, at the U.S. Department of Agriculture’s classing office, is plastic from the module wrap used to wrap cotton modules produced by the new John Deere round module harvesters. Despite diligent efforts by cotton ginning personnel to remove all plastic encountered during unwrapping of the seed cotton modules, plastic still finds a way into the cotton gin’s processing system. To help mitigate plastic contamination at the gin; an inspection system was developed that utilized low-cost color cameras to see plastic on the module feeder’s dispersing cylinders, that are normally hidden from view by the incoming feed of cotton modules. This technical note presents the design of an automated intelligent machine-vision guided cotton module-feeder inspection system. The system includes a machine-learning program that automatically detects plastic contamination in order to alert the cotton gin personnel as to the presence of plastic contamination on the module feeder’s dispersing cylinders. The system was tested throughout the entire 2019 cotton ginning season at two commercial cotton gins and at one gin in the 2018 ginning season. This note describes the over-all system and mechanical design and provides an over-view and coverage of key relevant issues. Included as an attachment to this technical note are all the mechanical engineering design files as well as the bill-of-materials part source list. A discussion of the observational impact the system had on reduction of plastic contamination is also addressed.


2017 ◽  
Vol 11 (4) ◽  
pp. 629-637 ◽  
Author(s):  
Kenichi Endo ◽  
◽  
Teruyuki Ishiwata ◽  
Tomohiro Yamazaki

This paper reports on the development of a low-cost machine vision inspection system to promote the wide employment of the system and foster further quality improvements in automobile manufacturing. The machine vision system consists of a camera that takes images of an inspection target, lighting to ensure appropriate illuminance, and a controller that analyzes the images and gives inspection results. By optimizing the performance and using free software, we succeeded in the development of an ultralow-cost machine vision system for one tenth of the cost of commercially available factory automation machine vision systems. The development and results are introduced in this paper. The applicability of the ultralow-cost machine vision system platform to applications other than inspection is also discussed.


2006 ◽  
Author(s):  
Naoshi Kondo ◽  
Junzo Kamata ◽  
Kazunori Ninomiya ◽  
Mitsuji Monta ◽  
K.C. Ting

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