Evaluation of learning vector quantization to classify cotton trash

Abstract

The cotton industry needs a method to identify the type of trash (nonlint material (NLM)) in cotton samples; learning vector quanti- zation (LVQ) is evaluated as that method. LVQ is a classification tech- nique that defines reference vectors (group prototypes) in an N-dimensional feature space (R N ). Normalized trash object features ex- tracted from images of compressed cotton samples defineR N.A n un- known NLM object is given the label of the closest reference vector (as defined by Euclidean distance). Different normalized feature spaces and NLM classifications are evaluated and accuracies reported for correctly identifying the NLM type. LVQ is used to partition cotton trash into: (1) bark (B), leaf (L), pepper (P), or stick (S); (2) bark and nonbark (N); or (3) bark, combined leaf and pepper (LP), or stick. Percentage accuracy for correctly identifying 139 pieces of test trash placed on laboratory pre- pared samples for the three scenarios are (B:95, L:87, P:100, S:88), (B:100, N:97), and (B:95, LP:99, S:88), respectively. Also, LVQ results are compared to previous work using backpropagating neural networks. © 1997 Society of Photo-Optical Instrumentation Engineers.