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With regard to the amount of production, after Egypt and Turkey, Iran possesses the third place in the world (FAO, 2012).
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Iran is the third largest producer and exporter of figs in the world and has ranked fourth in terms of cultivated area after Portugal, Egypt, and Turkey. Some varieties of Iranian edible figs are Izmir, General, and San Pedro. Introduction The common fig (Ficus carica L.) is a species of Moraceae family, native to the Mediterranean and western Asian. The system’s mean rate was 90 kg/h for processing and grading figs. Results showed that the developed system improved the sorting accuracy for all the classes up to 95.2%. In the grading algorithm, the values of these features were compared with the threshold value that was predetermined by an expert. 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. 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. This algorithm determined color intensity and diameter of each fig as the indicators of its color and size, respectively. For extracting the three quality indices of each class, a machine vision algorithm was developed. 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.
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Then, the images of the fig samples were captured using a machine vision system. Three quality indices, namely color, size, and split size, were first classified by fig-processing experts into the five classes. The system hardware was composed of a feeder, a belt conveyor, a CCD camera, a lighting system, and a separation unit. In this study, a grading system based on machine vision was developed for grading figs. Keywords: Dried figs Machine vision Grading Image processingĪ b s t r a c t Fig is a horticultural product which requires sorting at the postharvest stage before being marketed. Machine vision system for grading of dried figs Mehrdad Baigvand a, Ahmad Banakar a,⇑, Saeed Minaei a, Jalal Khodaei b, Nasser Behroozi-Khazaei b a bĭepartment of Biosystems Engineering, Tarbiat Modares University, Tehran, Iran Department of Biosystems Engineering, University of Kurdistan, Sanandaj, IranĪrticle history: Received 26 February 2015 Received in revised form 26 August 2015 Accepted 25 October 2015 Computers and Electronics in Agriculture 119 (2015) 158–165Ĭontents lists available at ScienceDirectĬomputers and Electronics in Agriculture journal homepage: Original papers