AN APPLE GRADING SYSTEM ACCORDING TO EUROPEAN FRUIT QUALITY STANDARDS USING GABOR FILTER AND ARTIFICIAL NEURAL NETWORKS
KEYVAN ASEFPOUR VAKILIAN *, JAFAR MASSAH University of Tehran, Department of Agrotechnology, College of Abouraihan, Tehran, Iran
*Corresponding author: keyvan.asefpour@ut.ac.ir
With the advent of applications of machine learning methods in food engineering in recent decades, several intelligent methods have been introduced in fruit grading technology. In this study, an apple grading system is presented using image’s textural features extraction and artificial intelligence. The objective of this study was to simplify the use of Gabor filter in classification of two varieties of apple fruits (Golden Delicious and Red Delicious) in four categories according to the European fruit quality standards. Using this filter, neural network classifier was trained for four category grading of the fruits. Two textural parameters were extracted from each obtained image: mean and variance of energy values of obtained image representing image’s luminous intensity and contrast, respectively. Experimental results indicated that the training of extracted features of about 350 fruits enabled the network to classify the test samples with appropriate accuracy. Compared to the state-of-the-art, the proposed grading categories (‘Extra’, ‘Type 1’, ‘Type 2’ and ‘Rejected’ classes) achieved acceptable recognition rates of about 89 % and 92 % overall accuracy for Golden Delicious and Red Delicious varieties, respectively. These experimental results show the appropriate application of proposed method in fast grading of apple fruits. Furthermore, proposed feature extraction and network training methods can be used efficiently in online applications.
