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United States Department of Agriculture

Agricultural Research Service

Title: IDENTIFICATION OF TRASH TYPES IN GINNED COTTON USING NEURO-FUZZY TECHNIQUES)

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

Submitted to: Institute of Electrical and Electronics Engineers Proceedings Fuzzy Systems
Publication Type: Proceedings
Publication Acceptance Date: 4/28/1999
Publication Date: N/A
Citation:

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. Work in the area of trash content computation and techniques to predict the trash content and compare with AMS measurements is currently ongoing.

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 trash (non-lint material/foreign matter) in ginned cotton. Lint is the cotton fiber; non-lint or foreign matter is everything other than lint. The effectiveness of a hybrid neuro-fuzzy structure, namely the Adaptive Network-Based Fuzzy Inference System (ANFIS) to classify trash types is compared to other techniques. Shape descriptors like shape factor, extent, and solidity measures are used to distinguish trash types.

Last Modified: 8/24/2016
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