Classification results for agricultural products are presented using a new neural network. This neural network inherently produces higher-order decision surfaces. It achieves this with fewer hidden layer neurons than other classifiers require. This gives better generalization. It uses new techniques to select the number of hidden layer neurons and adaptive algorithms that avoid other such ad hoc parameter selection problems and it allows selection of the best classifier parameters without the need to analyse the test set results. The agriculture case study considered is the inspection and classification of pistachio nuts using X-ray imagery. Present inspection techniques cannot provide good rejection of worm damaged nuts without rejecting too many good nuts. X-ray imagery has the potential to provide 100% inspection of such agricultural products in real time. Preliminary results presented indicate the potential to reduce major defects to 2% of the crop with only 1% of good nuts rejected. These results are preferable to present data. Future image processing techniques that should provide better features to improve performance and allow inspection of a larger variety of nuts are noted. These techniques and variations of them have uses in a number of other agricultural product inspection problems.
Document Type: Research Article
Carnegie Mellon University, Pittsburgh, PA, U.S.A. 2:
Western Regional Medical Research Center, ARS, U.S.A.