Machine vision grading of pistachio nuts using gray-level histogram

A. Ghazanfari, D. Wulfsohn, J. Irudayaraj

Research output: Contribution to journalArticlepeer-review


A machine vision system was used to classify "in the shell" pistachio nuts based on USDA grades. The gray-level histogram data obtained from the gray scale image of the nuts were analyzed to select a set of suitable recognition features. Based on the analyses, the mean of the gray-level histogram over 50 to 60 gray-level range and the area of each nut (the integral of its gray-level histogram) were selected as the recognition features. The selected features were used as input to three classification schemes: a Gaussian, a decision tree, and a multi-layer neural network (MLNN). The three classifiers had similar recognition rates. However, the MLNN classifier resulted in slightly higher performance with more uniform classification accuracy than the other two classifiers.

Original languageEnglish (US)
Pages (from-to)61-66
Number of pages6
JournalCanadian Agricultural Engineering
Issue number1
StatePublished - Jan 1998
Externally publishedYes


  • Classification
  • Machine vision
  • Neural networks
  • Pattern recognition
  • Pistachio nuts

ASJC Scopus subject areas

  • Bioengineering


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