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United States Department of Agriculture

Agricultural Research Service

Title: Identification of Trash Types and Computation of Trash Content in Ginned Cotton Using Soft Computing Techniques

item Siddaiah, Murali - LAS CRUCES
item Lieberman, Michael
item Prasad, Nadipuram - LAS CRUCES
item Hughs, Sidney

Submitted to: Midwest Symposium on Circuits and Systems
Publication Type: Proceedings
Publication Acceptance Date: May 5, 1999
Publication Date: August 31, 1999

Interpretive Summary: Research at the Southwestern Cotton Ginning Research Laboratory (SWCGRL) has resulted in improving our ability to identify trash categories of bark1 (fibrous), bark2 (non-fibrous), leaf, and pepper in cotton. The correlation between computed trash content and AMS measures of 100 cotton samples are reported.

Technical Abstract: The paper discusses the use of soft computing techniques such as neural networks and Fuzzy Logic based approaches in the identification of various types of trash (non-lint material/foreign matter), and the computation of trash content in ginned cotton. Lint is the cotton fiber; non-lint or foreign matter is everything other than lint. Trash content is the percentage of sample surface covered by non-lint particles. The effectiveness of a hybrid neuro-fuzzy structure, namely the Adaptive Network-Based Fuzzy Inference System (ANFIS) to classify trash types is compared with other techniques.

Last Modified: 4/22/2015
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