Grading pistachio nuts using a neural network approach

A. Ghazanfari, J. Irudayaraj, A. Kusalik

Research output: Contribution to journalArticlepeer-review


A multi-structure neural network (MSNN) classifier was proposed and applied to classify four varieties (classes) of pistachio nuts. The MSNN classifier consisted of four parallel discriminators (one per class), followed by a maximum selector. Each discriminator was a feed-forward neural network with two hidden layers and a single-neuron output layer. The discriminators were individually trained using physical attributes of the nuts extracted from their images as input. The performance of MSNN classifier was compared with the performance of a multi-layer feed-forward neural network (MLNN) classifier. The average classification accuracy of MSNN classifier was 95.9%, an increase of over 8.9% of the performance of MLNN.

Original languageEnglish (US)
Pages (from-to)2319-2324
Number of pages6
JournalTransactions of the American Society of Agricultural Engineers
Issue number6
StatePublished - 1996
Externally publishedYes


  • Classification
  • Machine vision
  • Neural network
  • Pattern
  • Physical attributes
  • Pistachio nuts

ASJC Scopus subject areas

  • Agricultural and Biological Sciences (miscellaneous)


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