Food Object Detection Using Custom-Trained YOLOv8 with Roboflow Integration
Received: 15 September 2025 | Revised: 6 October 2025 and 27 October 2025 | Accepted: 29 October 2025 | Online: 9 November 2025
Corresponding author: Rajesh Kumar S.
Abstract
This study presents a complete, real-time food detection pipeline integrating the YOLOv8 object detection framework and the Roboflow dataset management platform. A dataset with a total of 1,800 high resolution images of six food categories (egg, chicken, carrot, apple, bread, and burger) was developed by using Roboflow for annotation and augmentation, and it was trained through YOLOv8 in a PyTorch (Ultralytics) framework. Strong performance was reflected in experimental results, with a mean Average Precision (mAP@0.5) of above 92% and real-time processing speed of more than 25 FPS on NVIDIA T4 GPUs. Improvements of 5% in precision and 7% in recall were observed for the proposed approach compared to SSD and YOLOv3 baselines. Thus, the findings validated that the YOLOv8-Roboflow pipeline is a flexible and effective option for modern food detection applications in health monitoring and smart kitchen systems.
Keywords:
PyTorch (Ultralytics) framework, deep learning, food object detection, health applications, real-time detection, Roboflow, YOLOv8Downloads
References
A. Paul, R. Machavaram, Ambuj, D. Kumar, and H. Nagar, "Smart solutions for capsicum Harvesting: Unleashing the power of YOLO for Detection, Segmentation, growth stage Classification, Counting, and real-time mobile identification," Computers and Electronics in Agriculture, vol. 219, Apr. 2024, Art. no. 108832. DOI: https://doi.org/10.1016/j.compag.2024.108832
A. Paul and R. Machavaram, "Advanced segmentation models for automated capsicum peduncle detection in night-time greenhouse environments," Systems Science & Control Engineering, vol. 12, no. 1, Dec. 2024, Art. no. 2437162. DOI: https://doi.org/10.1080/21642583.2024.2437162
M. F. Bukhori, L. X. Xian, N. Zainal, and S. M. Myataza, "Development and Accuracy Evaluation of a YOLOv4-Based Food Detection Model for Smart IoT Refrigerators," Jurnal Kejuruteraan, vol. 36, no. 5, pp. 1849–1857, Sept. 2024. DOI: https://doi.org/10.17576/jkukm-2024-36(5)-06
F. Jubayer et al., "Detection of mold on the food surface using YOLOv5," Current Research in Food Science, vol. 4, pp. 724–728, 2021. DOI: https://doi.org/10.1016/j.crfs.2021.10.003
S. Madhu and V. RaviSankar, "Comprehensive Analysis of a YOLO-based Deep Learning Model for Cotton Plant Leaf Disease Detection," Engineering, Technology & Applied Science Research, vol. 15, no. 1, pp. 19947–19952, Feb. 2025. DOI: https://doi.org/10.48084/etasr.8944
Y. Egi, M. Hajyzadeh, and E. Eyceyurt, "Drone-Computer Communication Based Tomato Generative Organ Counting Model Using YOLO V5 and Deep-Sort," Agriculture, vol. 12, no. 9, Aug. 2022, Art. no. 1290. DOI: https://doi.org/10.3390/agriculture12091290
A. Kuznetsova, T. Maleva, and V. Soloviev, "Detecting Apples in Orchards Using YOLOv3 and YOLOv5 in General and Close-Up Images," in 17th International Symposium on Neural Networks, Cairo, Egypt, Dec. 2020. DOI: https://doi.org/10.1007/978-3-030-64221-1_20
E. Cengil and A. Çınar, "Poisonous Mushroom Detection using YOLOV5," Turkish Journal of Science and Technology, vol. 16, no. 1, pp. 119–127, Mar. 2021.
A. I. B. Parico and T. Ahamed, "Real Time Pear Fruit Detection and Counting Using YOLOv4 Models and Deep SORT," Sensors, vol. 21, no. 14, July 2021, Art. no. 4803. DOI: https://doi.org/10.3390/s21144803
A. F. Japar, H. R. Ramli, N. M. H. Norsahperi, and W. Z. W. Hasan, "Oil Palm Loose Fruit Detection Using YOLOv4 for an Autonomous Mobile Robot Collector," IEEE Access, vol. 12, pp. 138582–138593, Aug. 2024. DOI: https://doi.org/10.1109/ACCESS.2024.3446890
D. Feng, M. Liang, and G. Wang, "Improved YOLOv4 Based on Dilated Convolution and Focal Loss," in 2021 IEEE International Conference on Advances in Electrical Engineering and Computer Applications (AEECA), Dalian, China, Aug. 2021. DOI: https://doi.org/10.1109/AEECA52519.2021.9574147
J. Wang, Z. Gao, Y. Zhang, J. Zhou, J. Wu, and P. Li, "Real-Time Detection and Location of Potted Flowers Based on a ZED Camera and a YOLO V4-Tiny Deep Learning Algorithm," Horticulturae, vol. 8, no. 1, Dec. 2021, Art. no. 21. DOI: https://doi.org/10.3390/horticulturae8010021
G. Meng, J. Duan, Y. He, X. Ma, and N. Shen, "Application of Fish Classification, Counting, and Length Estimation: A Deep Learning Approach with Neural Networks and Computer Vision." Social Science Research Network, Rochester, NY, Dec. 31, 2024. DOI: https://doi.org/10.2139/ssrn.5067713
G. Liu, J. C. Nouaze, P. L. Touko Mbouembe, and J. H. Kim, "YOLO-Tomato: A Robust Algorithm for Tomato Detection Based on YOLOv3," Sensors, vol. 20, no. 7, Apr. 2020, Art. no. 2145. DOI: https://doi.org/10.3390/s20072145
B. Yan, P. Fan, X. Lei, Z. Liu, and F. Yang, "A Real-Time Apple Targets Detection Method for Picking Robot Based on Improved YOLOv5," Remote Sensing, vol. 13, no. 9, Apr. 2021, Art. no. 1619. DOI: https://doi.org/10.3390/rs13091619
S. Chen et al., "Recognition of rice seedling counts in UAV remote sensing images via the YOLO algorithm," Smart Agricultural Technology, vol. 12, Dec. 2025, Art. no. 101107. DOI: https://doi.org/10.1016/j.atech.2025.101107
J. Chen, H. Liu, Y. Zhang, D. Zhang, H. Ouyang, and X. Chen, "A Multiscale Lightweight and Efficient Model Based on YOLOv7: Applied to Citrus Orchard," Plants, vol. 11, no. 23, Nov. 2022, Art. no. 3260. DOI: https://doi.org/10.3390/plants11233260
L. Fu et al., "Kiwifruit detection in field images using Faster R-CNN with ZFNet," IFAC-PapersOnLine, vol. 51, no. 17, pp. 45–50, 2018. DOI: https://doi.org/10.1016/j.ifacol.2018.08.059
Z. He, M. Karkee, and P. Upadhyaya, "Detection of strawberries with varying maturity levels for robotic harvesting using YOLOv4," in American Society of Agricultural and Biological Engineers Annual International Meeting, ASABE 2021, July 2021. DOI: https://doi.org/10.13031/aim.202100051
https://github.com/Rajeshkumar3366/FoodProjectSixClasses.v1i.yolov8.
Food-101 – Mining Discriminative Components with Random Forests.
UEC-Food100.
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Copyright (c) 2025 Rajesh S. Kumar, Josephine Prem Kumar
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