Real-Time Classification of Local Orange Fruit Quality Using YOLO (You Only Look Once) and SVM (Support Vector Machine) Methods
DOI:
https://doi.org/10.62671/perfect.v2i2.55Keywords:
Oranges, consumers, fruits, classification, societyAbstract
Oranges are a fruit that we often encounter and are even consumed by people because of their various benefits. Oranges have commercial value in Indonesia and have a fairly wide reach. In order to increase competitiveness, oranges must also meet market standards, both domestic and foreign, so that they can be accepted by consumers. Of course, in this case, orange selection is very important. increasing sales and market competition by sellers, important indicators in selecting citrus fruit are in terms of size and color. In general, the selection of citrus fruit is done manually and based on human thinking, which causes several weaknesses that must be corrected, including requiring a long time, human visual limitations, and being influenced by human psychology itself. This is what causes inconsistencies in selection. oranges and does not comply with existing market requirements. So a research was carried out regarding the quality classification of local citrus fruit using the YOLO (You Only Look Once) and SVM (Support Vector Machine) methods in real time. In the comparison made between the two methods used, Yolo was found to be the best method for classifying citrus fruit.
References
Bhargava, A., & Bansal, A. (2021). Fruits and vegetables quality evaluation using computer vision: A review. Journal of King Saud University - Computer and Information Sciences, 33(3), 243–257. https://doi.org/https://doi.org/10.1016/j.jksuci.2018.06.002
Chen, M.-C., Cheng, Y.-T., & Liu, C.-Y. (2022). Implementation of a Fruit Quality Classification Application Using an Artificial Intelligence Algorithm. Sensors & Materials, 34.
Le, H. T. N., & Ngo, H. Q. T. (2025). Application of the vision-based deep learning technique for waste classification using the robotic manipulation system. International Journal of Cognitive Computing in Engineering, 6, 391–400.
Li, H., Wang, X., Bu, Y., David, C. C., & Chen, X. (2025). YOLOv8-Orah: An Improved Model for Postharvest Orah Mandarin (Citrus reticulata cv. Orah) Surface Defect Detection. In Agronomy (Vol. 15, Issue 4). https://doi.org/10.3390/agronomy15040891
Nosseir, A., & Ahmed, S. E. A. (2019). Automatic Classification for Fruits’ Types and Identification of Rotten Ones Using k-NN and SVM. International Journal of Online and Biomedical Engineering (IJOE), 15(03 SE-Papers), 47–61. https://doi.org/10.3991/ijoe.v15i03.9832
Preanto, S. A., Ahad, M. T., Emon, Y. R., Mustofa, S., & Alamin, M. (2024). A Semantic Segmentation Approach on Sweet Orange Leaf Diseases Detection Utilizing YOLO. ArXiv Preprint ArXiv:2409.06671.
Raj, R., Nagaraj, S. S., Ritesh, S., Thushar, T. A., & Aparanji, V. M. (2021). Fruit classification comparison based on cnn and yolo. IOP Conference Series: Materials Science and Engineering, 1187(1), 12031.
Taofik, A., Ismail, N., Gerhana, Y. A., Komarujaman, K., & Ramdhani, M. A. (2018). Design of smart system to detect ripeness of tomato and chili with new approach in data acquisition. IOP Conference Series: Materials Science and Engineering, 288, 12018.
Zhu, L., & Spachos, P. (2021). Support vector machine and YOLO for a mobile food grading system. Internet of Things, 13, 100359. https://doi.org/https://doi.org/10.1016/j.iot.2021.100359
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Muhammad Khoiruddin Harahap, Rudi Arif Candra, Arie Budiansyah, Romulo P. Aritonang, Zulfan, Devi Satria Saputra (Author)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
