Machine vision system for grading of dried figs

Abstract

Fig is a horticultural product which requires sorting at the postharvest stage before being marketed. In this study, a grading system based on machine vision was developed for grading figs. The system hardware was composed of a feeder, a belt conveyor, a CCD camera, a lighting system, and a separation unit. Three quality indices, namely color, size, and split size, were first classified by fig-processing experts into the five classes. Then, the images of the fig samples were captured using a machine vision system. First, the length of pixels in each image and longitudinal coordinates of the center of gravity of fig pixels were extracted for calculating the nozzle eject time. For extracting the three quality indices of each class, a machine vision algorithm was developed. This algorithm determined color intensity and diameter of each fig as the indicators of its color and size, respectively. For calculating the split area, the images were first binarized by using the color intensity difference between the split and other parts of the fruit in order to determine the area of the split section. A grading algorithm was also coded in Lab-VIEW for sorting figs based on their quality indices extracted by the image processing algorithm into five qualitative grades. In the grading algorithm, the values of these features were compared with the threshold value that was predetermined by an expert. Results showed that the developed system improved the sorting accuracy for all the classes up to 95.2%. The system’s mean rate was 90kg/h for processing and grading figs.