Comparison of a neural network and traditional classifier for machine vision inspection of potatoes

S. H. Deck, C. T. Morrow, P. H. Heinemann, H. J. Sommer

Research output: Contribution to journalArticlepeer-review

28 Scopus citations


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, andshatter bruise in two potato cultivars. The backpropagation network's 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.

Original languageEnglish (US)
Pages (from-to)319-326
Number of pages8
JournalApplied Engineering in Agriculture
Issue number2
StatePublished - Mar 1995

All Science Journal Classification (ASJC) codes

  • General Engineering


Dive into the research topics of 'Comparison of a neural network and traditional classifier for machine vision inspection of potatoes'. Together they form a unique fingerprint.

Cite this