Cashews whole and splits classification using a novel machine vision approach

S. Sunoj, C. Igathinathane, S. Jenicka

Research output: Contribution to journalArticlepeer-review

Abstract

Cashew is a high-value and the third largest edible tree nut that is universally consumed as a snack or included in food preparations. Classification of whole and split cashews in industries is carried out manually by visual inspection and hand-picking, which is time-consuming, tedious, challenging, and laborious. A machine vision methodology of capturing cashew shadows and associated image processing ImageJ plugin to classify cashews into whole and splits were developed and tested. The developed classification algorithm was based on a novel idea of using surface grayscale-intensity-profile for split-up cashews and object shadows for split-down and whole cashews. Out of several features derived from grayscale-intensity-profile values, the “length of curve” best classified the split-up cashews from others. The challenge of classifying the whole and split-down cashews was addressed by a new “shadow to total-area ratio.” An accuracy of 100 % was achieved by the algorithm. A process scale-up by increasing the height and power of light source was also proposed. The promising results suggest that the developed algorithm can be coupled with a suitable hardware system to perform accurate classification of the whole and split cashews.

Original languageEnglish (US)
Pages (from-to)19-30
Number of pages12
JournalPostharvest Biology and Technology
Volume138
DOIs
StatePublished - Apr 2018
Externally publishedYes

Keywords

  • Cashew
  • Grading
  • Image processing
  • ImageJ
  • Machine vision
  • Process system

ASJC Scopus subject areas

  • Food Science
  • Agronomy and Crop Science
  • Horticulture

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