Automation in cotton ginning

  1. Murali Siddaiah1,*,
  2. Michael A. Lieberman1,
  3. Nadipuram R. Prasad2,
  4. S. E. Hughs1

Article first published online: 12 JAN 2004

DOI: 10.1002/int.10156

Issue

International Journal of Intelligent Systems

International Journal of Intelligent Systems

Special Issue: Intelligent Technologies

Volume 19, Issue 1-2, pages 111–129, January - February 2004

Additional Information

How to Cite

Siddaiah, M., Lieberman, M. A., Prasad, N. R. and Hughs, S. E. (2004), Automation in cotton ginning. Int. J. Intell. Syst., 19: 111–129. doi: 10.1002/int.10156

Author Information

  1. 1

    Southwestern Cotton Ginning Research Laboratory, U.S. Department of Agriculture—Agricultural Research Service, Mesilla Park, NM 88047

  2. 2

    Klipsch School of Electrical and Computer Engineering, Las Cruces, NM 88003

*Southwestern Cotton Ginning Research Laboratory, U.S. Department of Agriculture—Agricultural Research Service, Mesilla Park, NM 88047

Publication History

  1. Issue published online: 12 JAN 2004
  2. Article first published online: 12 JAN 2004

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Abstract

This article discusses a framework for implementation of a machine vision-based system for on-line identification of trash objects commonly found in cotton. Soft computing techniques such as neural networks and fuzzy inference systems can classify trash objects into individual categories such as bark, stick, leaf, and pepper trash types with great accuracy. This identification of trash objects to individual categories can be used for the dynamic allocation of trash-extraction equipment during the ginning process. Such a system can be implemented in a modern gin to configure an optimal set of equipment during ginning to produce quality cotton. Classification of cotton in real time allows for an automated means for assignment of trash grades to cotton and could have a significant impact on the entire cotton industry. © 2004 Wiley Periodicals, Inc.

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