Pattern recognition models for spectral reflectance evaluation of apple blemishes

Surface blemishes of various apple varieties were analyzed by their reflectance characteristics between 460 and 1130 nm. Normalized reflectance data were collected at 10 nm increments with liquid crystal tunable filters. Data were utilized as input values for various pattern recognition models specifically multi-layer back propagation, unimodal Gaussian, K-nearest neighbor and nearest cluster algorithms. Partitioning data into 50:50 training and test sets, correct classification in separating unflawed versus blemished areas ranged from 62 to 96% (Year I) and from 73 to 85% (Year II). The algorithm which yielded the highest correct classification was the multi-layer back propagation but minor variation was found for number of hidden nodes or neural net architecture. (C) 1998 Elsevier Science B.V. All rights reserved.