Comparison of a Neural Network and Traditional Classifier for Machine Vision Inspection of Potatoes

This work addressed the relative strengths and weaknesses of the backpropagation neural network versus the Fisher discriminant function. Their performance was compared for machine vision inspection of greening, shape, and shatter bruise in two potato cultivars. The backpropagation networks number of hidden nodes were varied from zero to eight for each defect type to determine the optimal network classification size. The network was trained and tested five times at each hidden node number and defect type to minimize local minima variation. For greening, the best backpropagation network averaged 74.0% with three hidden nodes while the Fisher method performed with a 70.0% accuracy. The backpropagation method also performed better for shape discrimination with a 73.3% average accuracy at seven hidden layer nodes versus a 68.1% accuracy. The Fisher method performed better for shatter bruise detection with a 76.7% accuracy versus a 56.0% average accuracy at four hidden layer nodes for backpropagation.