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Color Grading of Cotton-Grading (Part II)

Luo Cheng, Kermit E. Duckett, Terezie Zapletalova, Michael Watson and Hossein Ghorashi


 
ABSTRACT

In this part of the series, two color grading systems were developed using expert system and neural networks. Both grading systems have two modes of operation, classification mode and training mode. In the training mode, the expert system can be trained by a statistical method based on Bayes' theorem or genetic algorithm. For neural network approach, the grading system can be trained by backpropagation algorithm or probabilistic neural network. Using 100 cotton samples from USDA, the agreement with classer can be improved from the original 50% from HVI grading to 86% - 100% depending on the training method and the training samples. The relative contributions of each measurement on color grading were also investigated using stepwise discriminate analysis.



Reprinted from Proceedings of the 1999 Beltwide Cotton Conferences pp. 649 - 653
©National Cotton Council, Memphis TN

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Document last modified Monday, Jun 21 1999