|Siddaiah, Murali - NMSU, LAS CRUCES, NM|
|Prasad, Nadipuram - NMSU, LAS CRUCES, NM|
Submitted to: National Cotton Council Beltwide Cotton Conference
Publication Type: Proceedings
Publication Acceptance Date: January 8, 2002
Publication Date: June 27, 2002
Citation: Siddaiah, M., Lieberman, M.A., Hughs, S.E., Prasad, N.R. Automation in cotton ginning. National Cotton Council Beltwide Cotton Conference. 2002. 14 p. Interpretive Summary: Current cotton ginning practices do not take into consideration of the types of trash or the quantity of trash objects when cleaning machines (inclined cleaners, stick machines, lint cleaners, etc.) are included in the ginning process. The identification of the trash types and the quantity of the trash objects at various stages of the ginning process can be used to the allocation of necessary equipment in the cleaning process. The identification of trash objects in real-time can be used in the automatic allocation of cleaning equipments by including or excluding them during the process to obtain superior quality cotton.
Technical Abstract: This paper 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 accuracies. 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 alllows for an automated means for assignment of trash grades to cotton, and could have a significant impact on the entire cotton industry.