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APPLE SORTING USING ARTIFICIAL NEURAL NETWORKS AND SPECTRAL IMAGING
Published by the American Society of Agricultural and Biological Engineers, St. Joseph, Michigan www.asabe.orgCitation: Transactions of the ASABE. Vol. 45(6): 1995–2005 . @2002
Authors: I. Kavdir, D. E. Guyer
Keywords: Pixel gray values, Texture, Backpropagation neural networks, Spectral band (wavelength) selection
Empire and Golden Delicious apples were sorted based on their surface quality conditions using backpropagation neural networks. Pixel gray values and texture features obtained from the entire apple image were used as input to artificial neural network classifiers. Two classification applications were performed: a 2–class classification that included a defective (or stem/calyx) apple group and a good apple group, and a 5–class classification that included all the defective and good apple groups. Effective image resolution was evaluated to shorten the training and testing times in classification with neural networks. Resolution size of 60 Ü 80 pixels was identified to be efficient and used in all of the classification applications. Effective spectral bands for identification of specific surface characteristics were determined in the 2–class and 5–class classification applications. Artificial neural network classifiers successfully separated apples with defects from non–defective apples without confusing the stem/calyx with defects. Classification success in the 2–class classification ranged from 89.2% to 100%. In the 5–class classification, classification success for Empire apples was between 93.8% and 100%, while classification success for Golden Delicious apples was between 89.7% and 94.9% based on the features used.