EMBEDDED-PLATFORM-BASED FRUIT IDENTIFICATION  USING YOLOv11 MODEL

Lê Hùng Linh; Ngô Hữu Huy; Mẫn Bá Tuyên; Nguyễn Thành Nam; Nguyễn Thị Mai Khuyên

doi:10.34238/tnu-jst.11825

EMBEDDED-PLATFORM-BASED FRUIT IDENTIFICATION USING YOLOv11 MODEL

About this article

Received: 06/01/25 Revised: 19/03/25 Published: 21/03/25

Authors

1. Le Hung Linh, TNU - University of Information and Communication Technology
2. Ngo Huu Huy , TNU - University of Information and Communication Technology
3. Man Ba Tuyen, TNU - University of Information and Communication Technology
4. Nguyen Thanh Nam, TNU - University of Information and Communication Technology
5. Nguyen Thi Mai Khuyen, Hanoi Metropolitan University

Abstract

In the context of agricultural modernization, automatic fruit identification and classification are becoming increasingly important to optimize production processes and supply chain management. This study presents a fruit recognition system using YOLOv11 model based on embedded platform. The system is deployed on a Raspberry Pi 4 Model B device, allowing on-site data processing, contributing to minimizing latency and dependence on internet connection. The database includes 2,500 images of five types of fruit: orange, strawberry, grape, apple, and mango. The training results show high accuracy, reaching a mAP50 value of 0.935 after 50 epochs, demonstrating the optimization ability of the model. During testing, the system demonstrated its ability to accurately identify fruits. These results confirm the potential of edge computing technology in improving agricultural production efficiency.

Keywords

Edge computing; Fruit identification; Raspberry Pi; Smart agriculture; YOLOv11

Full Text:

PDF (Tiếng Việt)

References

[1] M. A. Shahin, E. W. Tollner, R. W. McClendon, and H. R. Arabnia, “Apple Classification Based on Surface Bruises Using Image Processing and Neural Networks,” Transactions of the ASAE, vol. 45, no. 5, pp. 1619-1627, 2002.

[2] M. R. Mia, M. J. Mia, A. Majumder, S. Supriya, and Md. T. Habib, “Computer Vision Based Local Fruit Recognition,” International Journal of Engineering and Advanced Technology, vol. 9, no. 1, pp. 2810-2820, 2019.

[3] S. U. Amin and M. S. Hossain, “Edge Intelligence and Internet of Things in Healthcare: A Survey,” IEEE Access, vol. 9, pp. 45-59, 2021.

[4] A. Koukounaras, “Advanced Greenhouse Horticulture: New Technologies and Cultivation Practices,” Horticulturae, vol. 7, no. 1, pp. 1-5, 2021.

[5] Y. Kalyani and R. Collier, “A Systematic Survey on the Role of Cloud, Fog, and Edge Computing Combination in Smart Agriculture,” Sensors, vol. 21, no. 17, pp. 1-27, 2021.

[6] D. Yang, J. Wu, and Y. He, “Optimizing the Agricultural Internet of Things (IoT) with Edge Computing and Low-Altitude Platform Stations,” Sensors, vol. 24, no. 21, pp. 1-11, 2024.

[7] P. Dhiman, A. Kaur, Y. Hamid, E. Alabdulkreem, H. Elmannai, and N. Ababneh, “Smart Disease Detection System for Citrus Fruits Using Deep Learning with Edge Computing,” Sustainability, vol. 15, no. 5, pp. 1-18, 2023.

[8] M. S. Hossain, G. Muhammad, and S. U. Amin, “Improving Consumer Satisfaction in Smart Cities Using Edge Computing and Caching: A Case Study Of Date Fruits Classification,” Future Generation Computer Systems, vol. 88, pp. 333-341, 2018.

[9] A. Kumar and D. Singh, “Detection of Security Attacks on Edge Computing of IoT Devices through NS2 Simulation,” Journal of Physics: Conference Series, vol. 2327, no. 1, pp. 1-9, 2022.

[10] R. P. Sishodia, R. L. Ray, and S. K. Singh, “Applications of Remote Sensing in Precision Agriculture: A Review,” Remote Sensing, vol. 12, no. 19, pp. 1-31, 2020.

[11] A. Balafoutis, B. Beck, S. Fountas, J. Vangeyte, T. V. der Wal, I. Soto, M. Gómez-Barbero, A. Barnes, and V. Eory, “Precision Agriculture Technologies Positively Contributing to GHG Emissions Mitigation, Farm Productivity and Economics,” Sustainability, vol. 9, no. 8, pp. 1-28, 2017.

[12] C.-W. Hsu, Y.-H. Huang, and N.-F. Huang, “Real-time Dragonfruit’s Ripeness Classification System with Edge Computing Based on Convolution Neural Network,” in Proc. 2022 International Conference on Information Networking (ICOIN), 2022, pp. 177-182.

[13] S. Raza, S. Wang, M. Ahmed, and M. R. Anwar, “A Survey on Vehicular Edge Computing: Architecture, Applications, Technical Issues, and Future Directions,” Wireless Communications and Mobile Computing, vol. 2019, pp. 1-19, 2019.

[14] ONNX, “Introduction to ONNX, Ultralytics,” 2024. [Online]. Available: https://onnx.ai/onnx/intro/. [Accessed Nov. 20, 2024].

[15] Raspberry Pi, “Raspberry Pi 4 Model B,” 2024. [Online]. Available: https://www.raspberrypi.com/ products/raspberry-pi-4-model-b/specifications/. [Accessed Dec. 22, 2024].

[16] Ultralytics, “Ultralytics YOLO11,” 2024. [Online]. Available: https://docs.ultralytics.com/ models/yolo11/. [Accessed Nov. 20, 2024].

[17] Roboflow, “Oranges Computer Vision Project,” 2024. [Online]. Available: https://universe.roboflow.com/fruits-classification-hboap/oranges-1ijib. [Accessed Nov. 20, 2024].

[18] Roboflow, “Strawbery Computer Vision Project,” 2024. [Online]. Available: https://universe.roboflow.com/daud-rizal-ghshq/strawbery23. [Accessed Nov. 20, 2024].

[19] Roboflow, “Fruits Computer Vision Project,” 2024. [Online]. Available: https://universe.roboflow.com/arab-academy-for-science-technology-maritime-transport-smart-village-campus-he7gz/fruits-n6t7m. [Accessed Nov. 20, 2024].

[20] Roboflow, “Apple Computer Vision Project,” 2024. [Online]. Available: https://universe.roboflow.com/tuyenmb/apple-f3ujh. [Accessed Nov. 20, 2024].

[21] Roboflow, “Mango Computer Vision Project,” 2024. [Online]. Available: https://universe.roboflow.com/tuyenmb/mango-cdxx4. [Accessed Nov. 20, 2024].

DOI: https://doi.org/10.34238/tnu-jst.11825

Refbacks

There are currently no refbacks.



Remember me