International Research Journal of Innovations in Engineering and Technology - IRJIET International Open Access, Monthly, Peer Reviewed, Reputed Journal ISSN (online): 2581-3048

Impact Factor (2025): 6.9

DOI Prefix: 10.47001/IRJIET

Automated Orange Detection Using YOLO on Raspberry Pi

Abstract

This research paper describes a real-time orange identification system based on the YOLO (You Only Look Once) object detection method and implemented on a Raspberry Pi. The technology attempts to improve agricultural efficiency by automating the detection and counting of oranges, saving manual labor and increasing accuracy. The YOLO model has been improved for deployment on the Raspberry Pi, ensuring real-time performance with limited processing resources. Experimental results show that the system performs well in a variety of environments, with excellent accuracy and processing speed suitable for agricultural applications.

Country : India

1 Tabassum H Khan2 Maithili Manekar3 Nainita Ramkelkar4 Vaishanavi Karadbhajne5 Saniya Bhagat6 Pranjal Kohaley

  1. Associate Professor, Department of Artificial Intelligence, G H Raisoni College of Engineering and Managemant, Nagpur, India
  2. Student, Department of Artificial Intelligence, G H Raisoni College of Engineering and Management, Nagpur, India
  3. Student, Department of Artificial Intelligence, G H Raisoni College of Engineering and Management, Nagpur, India
  4. Student, Department of Artificial Intelligence, G H Raisoni College of Engineering and Management, Nagpur, India
  5. Student, Department of Artificial Intelligence, G H Raisoni College of Engineering and Management, Nagpur, India
  6. Student, Department of Artificial Intelligence, G H Raisoni College of Engineering and Management, Nagpur, India

IRJIET, Volume 8, Issue 12, December 2024 pp. 66-72

doi.org/10.47001/IRJIET/2024.812011

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References

  1. Garg, S., et al. "Real-Time Fruit Detection Using YOLO on Raspberry Pi." Journal of Agricultural Engineering, 2023.
  2. Morshed, A., et al. "Implementation of YOLOv3 for Fruit Detection on Low-Cost Devices." Computing in Agriculture, 2023.
  3. Sa, I., et al. "Deep Neural Networks for Fruit Detection Using RGB-D in Orchards." Precision Agriculture, 2016.
  4. Redmon, J., et al. "You Only Look Once: Unified, Real-Time Object Detection." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016.
  5. Singh, D., et al. "Automation in Agriculture Using Machine Learning Algorithms." International Journal of Automation in Agriculture, 2022.
  6. Koirala, A., et al. "Deep Learning for Real-Time Fruit Detection in Orchards." Frontiers in Plant Science, 2019.
  7. Liu, X., et al. "Object Detection Algorithms for Fruit Harvesting Robotics." Journal of Field Robotics, 2021.
  8. Lee, K., et al. "Fruit Counting in Orchards Using Raspberry Pi with YOLOv3." Smart Farming Technologies, 2022.
  9. Nguyen, T., et al. "Performance Optimization of YOLO Models on Low-Cost Platforms." Journal of Computational Agriculture, 2020.
  10. Zhou, J., et al. "Efficient Object Detection in Agriculture Using YOLOv4." Journal of Agricultural Science, 2021.
  11. Rao, P., et al. "Machine Vision for Automated Fruit Harvesting." Journal of Robotics in Agriculture, 2020.
  12. Wang, Y., et al. "Challenges and Opportunities of Deep Learning in Agriculture." Computational Intelligence in Agriculture, 2021.
  13. Smith, H., et al. "Pruning and Quantization for Deep Learning Models in Agriculture." AI in Agriculture, 2022.
  14. Tang, J., et al. "Comparative Study of Object Detection Models in Agriculture." Journal of Agricultural Technology, 2023.
  15. Lu, Y., et al. "Impact of Occlusion on Fruit Detection Systems." Journal of Agricultural Automation, 2021.
  16. Khan, M., et al. "Improving Real-Time Fruit Detection with YOLO Models." International Journal of Advanced Agricultural Research, 2023.
  17. Nguyen, Q., et al. "Real-Time Fruit Quality Assessment Using Computer Vision." Journal of Digital Agriculture, 2020.
  18. Patel, R., et al. "Deploying YOLOv5 on Edge Devices for Fruit Detection." Journal of Embedded Agriculture Systems, 2022.
  19. Zhao, L., et al. "Scalable Fruit Detection Using Deep Learning." Journal of Smart Agriculture, 2021.
  20. Tran, H., et al. "Optimizing Deep Learning Models for Raspberry Pi." Journal of Precision Farming, 2021.
  21. Zhang, W., et al. "YOLO-Based Fruit Detection: A Survey." Agricultural Engineering Journal, 2022.
  22. Yao, X., et al. "Fruit Detection in Dynamic Environments." IEEE Transactions on Automation Science and Engineering, 2020.
  23. Banu, M., et al. "Automated Orchard Systems with Machine Learning." Journal of Agronomy and Horticulture, 2023.
  24. Garcia, R., et al. "Object Detection in Agricultural Robotics." Journal of Robotic Agriculture, 2019.
  25. Ali, S., et al. "Machine Learning in Orchard Management." Computational Agriculture Journal, 2021.
  26. Oliver, A., et al. "The Future of Smart Farming with Deep Learning." International Journal of Agricultural Technology, 2022.
  27. White, P., et al. "Machine Learning Applications for Agricultural Robotics." Agriculture and Automation Journal, 2020.
  28. Reddy, V., et al. "Efficient Fruit Harvesting Using Machine Vision Systems." Journal of Smart Technologies in Agriculture, 2023.
  29. Carter, T., et al. "Advancements in Machine Learning for Agriculture." AI and Robotics in Agriculture, 2022.
  30. Liu, G., et al. "Fruit Detection Accuracy in Low-Cost Devices." Journal of Digital Farming, 2020.

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