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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

Author
item Siddaiah, Murali
item Lieberman, Michael
item Prasad, Nadipuram
item Hughs, Sidney

Submitted to: Midwest Symposium on Circuits and Systems
Publication Type: Proceedings
Publication Acceptance Date: 5/5/1999
Publication Date: N/A
Citation: N/A

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: 10/18/2017
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