Raisin Grading by Machine Vision

A machine vision system for grading raisins was developed, including an imaging test stand and image analysis algorithms, which identified wrinkle edge density, average gradient magnitude, angularity, and elongation as key features of raisin maturity.

Abstract

A machine vision system for grading raisins was developed, including an imaging test stand and image analysis algorithms. Raisin maturity is mainly based on visual features such as degree of wrinkles and shape. The features used in the image analysis were wrinkle edge density, average gradient magnitude, angularity, and elongation. A Bayes classifier was used to separate the raisins into three grades: B or better, C, and substandard.