Artificial intelligence classifiers for sorting apples based on watercore

Statistical classification methods, such as the Bayesian classifier, can pr ovide optimal classification but their performance depends heavily on the a ssumption of normality of the input data. Artificial intelligence (Al) appr oaches, on the other hand, entail less stringent assumptions about the stat istical characteristics of the input data. Hence, the neural network and fu zzy logic classifiers are expected to perform better than the Bayesian clas sifier for a given data set. This paper describes steps involved in the dev elopment of an optimal neural network classifier and a fuzzy classifier for sorting apples using the selected image features as the input variables. P erformance of the Al classifiers developed was compared with that of the Ba yesian classifier using the same data set. The fuzzy classifier (80%) perfo rmed as well as the Bayesian classifier with linear discriminant functions (79%), whereas, the neural classifier performed better (88%). (C) 2001 Sils oe Research Institute.