Applied Linear Discriminant Analysis and Artificial Neural Network for Sorting Dried Figs Based on Texture Properties

Dried figs are one of the horticultural products that require sorting in the postharvest stage in order to be presented to the market. In Iran, figs are graded manually by professional workers or automatically by mechanical machines. This paper presents a new algorithm based on machine vision technology applicable to be installed in the fig sorting machines. In the presented methodology, image texture properties of figs are extracted by an image processing algorithm. Some features selected by stepwise linear discriminant analysis were introduced as the superior ones for discriminating different classes of dried figs. Among the ten features, discriminant analysis selected six. The selected texture features were fed to artificial neural networks in order to implement the classification process. The image processing assisted neural networks methodology showed promising result as the total sorting accuracy was 100%.