Automatic apple grading model development based on back propagation neural network and machine vision, and its performance evaluation

Abstract

This paper describes a new apple classification system based on machine vision and artificial neural network (ANN), which classifies apple in real time on the basis of physical parameters of apple such as size, color and external defects. A specific hardware subsystem has been developed and described for every stage of input and output. The hardware subsystem is interfaced with the software to make the whole system automatic. The purpose of this paper is to automate apple classification. Presently, ANN is used in a wide range of classification applications. We have trained a back-propagation neural network to classify apple. Two sets of variables are used for the training purpose. First set is the independent variable, which is the surface level apple quality parameter. Second set is the dependent variable, which is the quality of the apple. The results of ANN model are discussed; however, the modeling results showed that there is an excellent agreement between the experimental data and predicted values, with a high determination coefficient, very good performance, fewer parameters, shorter calculation time and lower prediction error. The classification accuracy achieved is high, showing that a neural network is capable of making such classification. A low level of errors in classification confirmed that the neural network models are an effective instrument for apple classification. This model might be an alternative method for assessing the quality of apple and provide consumers with a safer food supply.