Development of Multi-Product Grading System

Abstract

It is anticipated that grading system of agricultural product, which gives us many kinds ofinformation such as size, color, shape, defect, and internal quality, will be important from the viewpoint of traceability in future. Many grading systems have been developed and practically used forfruits and vegetables in Japan. They play roles to be substituting for human labor with precision byuse of machine vision, NIR analysis, and automation technologies. Japanese farmers often producevarious agricultural products in small quantity for short seasons annually, while the high capacitygrading systems are used for specific products therefore the annual operating period is very less. It isbecoming obvious that the multi-product grading system is needed.<br><br>In this paper, a multi-product grading system is developed. Initially, fruits or vegetables such astomato and orange will be arriving randomly through the conveyor up-to a CCD color camera fixed ata height of 80 cm. Based on the Information through this camera robot will move to pick the object byusing suction pad. Then, robot carries the fruit to grade it by using two CCD color cameras. One is positioned horizontally and the other one vertically upside-down. The horizontal camera is used topredict the color percentage, bruise and other defects on the surface of the fruit. Whereas bottomcamera does the processing for the bottom part of the fruit, such as calyx and any defect affectingthe grade of the products. Surface and bottom information of fruit will be given as input to thedeveloped neural network. Three grading levels i.e., grade A grade B and grade C are assigned asoutput to the neural network. Thus three boxes were kept in specific order to place the fruits. Thispaper shows that neural network can be applied as a tool to grade the fruits with good accuracy.Future study is needed to improve the proposed grading model, to develop the grading methods foreggplant and process the information to implement the traceability of fruits in the system.