A method for classifying oranges based on image processing and neural networks

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RASHIDI HASSAN; Esmaili Faride; KHOJASTEHNAZHAND MOSTAFA

مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Journal Paper

Paper Information

Journal: Food and Health Year:2022 | Volume:5 | Issue:1 Page(s): 9-16

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Information Journal Paper

Title

A method for classifying oranges based on image processing and neural networks

Author(s)

RASHIDI HASSAN | Esmaili Faride | KHOJASTEHNAZHAND MOSTAFA | Issue Writer Certificate

Keywords

Orange

Co-occurrence Matrix

Image processing

Machine Vision

Defects

Abstract

In recent days, there have been many recommendations on social media about eating healthy fruits to strengthen the immune system and corona resistance. Therefore, it is very important to identify spoiled fruits at this time when human society is concerned about coronavirus and the human body needs healthy fruits in case of this disease. This paper proposes a method to identify the type of Defects found in Oranges fruits. We used a Machine Vision system to capture sample images, which includes a charge-coupled device camera, black box, lighting system, and personal computer. The citrus fruits are classified into eight classes, including Wind scar, Stem-end breakdown, Snail bites, Thrips scar, Scale injury, Medfly, Rings, and Calyx, depending on the type and model of the Defects. In the proposed method, classification by the neural network with the help of Co-occurrence Matrix for four angles θ, =0°, , 45°, , 90°, , and 135°, , were extracted to identify various Defects and 24 features related to the areas with defect in citrus. For the final classification of Defects in citrus, after evaluating many classification tools from various tools available, Feed-forward Back Propagation Neural Network (FFBPNN) is used. The result of the neural network classifier was obtained with the help of the Co-occurrence Matrix by taking four angles (horizontal, right diagonal, vertical, and left diagonal) with an accuracy of 89. 65%. The evaluation shows acceptable results compared with similar studies. It is a reliable method in the food classification industry with reasonable accuracy.

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APA: Copy

RASHIDI, HASSAN, Esmaili, Faride, & KHOJASTEHNAZHAND, MOSTAFA. (2022). A method for classifying oranges based on image processing and neural networks. FOOD AND HEALTH JOURNAL, 5(1), 9-16. SID. https://sid.ir/paper/1007119/en

Vancouver: Copy

RASHIDI HASSAN, Esmaili Faride, KHOJASTEHNAZHAND MOSTAFA. A method for classifying oranges based on image processing and neural networks. FOOD AND HEALTH JOURNAL[Internet]. 2022;5(1):9-16. Available from: https://sid.ir/paper/1007119/en

IEEE: Copy

HASSAN RASHIDI, Faride Esmaili, and MOSTAFA KHOJASTEHNAZHAND, “A method for classifying oranges based on image processing and neural networks,” FOOD AND HEALTH JOURNAL, vol. 5, no. 1, pp. 9–16, 2022, [Online]. Available: https://sid.ir/paper/1007119/en

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