Application of YOLO and Custom-Designed Intelligent Teaching Aids in Robotic Arm-Based Fruit Classification and Grasping Instruction

Authors

  • Chun Chieh Wang
  • Sun-Jing Yan

DOI:

https://doi.org/10.22399/ijcesen.3319

Keywords:

Fruit Recognition, Robotic Arm, YOLOv4, Deep Learning

Abstract

With the growing impact of deep learning and computer vision, real-time image recognition and robotic systems have become increasingly important in fields such as autonomous vehicles and smart devices. In this study, a practical teaching module was developed by integrating the YOLO (You Only Look Once) algorithm, robotic arm control, and local crop recognition. The proposed system enables automated fruit detection, classification, and sorting using a six-axis robotic arm. This hands-on approach allows students to apply artificial intelligence in real-world agricultural contexts, thereby enhancing their understanding of smart farming technologies. The module supports both theoretical learning and skill development in automation and intelligent systems, aligning with future trends in AI-based agriculture and industrial applications.

References

[1] Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2016). You only look once: Unified, real-time object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 779–788.

[2]Redmon, J., & Farhadi, A. (2017). YOLO9000: Better, faster, stronger. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 7263–7271.

[3] Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2016). You only look once: Unified, real-time object detection. IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 779–788.

[4]Bochkovskiy, A., Wang, C.-Y., & Liao, H.-Y. M. (2020). YOLOv4: Optimal speed and accuracy of object detection. arXiv preprint arXiv:2004.10934. https://arxiv.org/abs/2004.10934

[5] Wang, L., Zhou, K., Chu, A., Wang, G., & Wang, L. (2021). An improved light-weight traffic sign recognition algorithm based on YOLOv4-tiny. IEEE Access, 9, 124963–124971. https://doi.org/10.1109/ACCESS.2021.3109882

[6] Safonova, A., Hamad, Y., Alekhina, A., & Kaplun, D. (2022). Detection of Norway spruce trees (Picea abies) infested by bark beetle in UAV images using YOLOs architectures. IEEE Access, 10, 10384–10392. https://doi.org/10.1109/ACCESS.2022.3143981

[7] Reis, D., Kupec, J., Hong, J., & Daoudi, A. (2023). Real-time flying object detection with YOLOv8. arXiv preprint arXiv:2305.09972. https://arxiv.org/abs/2305.09972

[8] Zhong, J., Qian, H., Wang, H., Wang, W., & Zhou, Y. (2024). Improved real-time object detection method based on YOLOv8: A refined approach. Journal of Real-Time Image Processing, 22(4). https://doi.org/10.1007/s11554-024-01358-4

[9] Wang, Y.-S. (2020). Adaptive inverse dynamics motion control with image-based visual servoing for UR5 manipulator (Master’s thesis, National Dong Hwa University, Taiwan).

[10] Wang, C.-Y., Bochkovskiy, A., & Liao, H.-Y. M. (2023). YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. Proceedings of the 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 7464–7475.

[11] Chiu, F.-W. (2024). Comparison of detection results of bird's nests on transmission line towers by YOLOv3, YOLOv4 and YOLOv5 (Master’s thesis, National Changhua University of Education, Taiwan).

Downloads

Published

2025-07-10

How to Cite

Chieh Wang, C., & Sun-Jing Yan. (2025). Application of YOLO and Custom-Designed Intelligent Teaching Aids in Robotic Arm-Based Fruit Classification and Grasping Instruction. International Journal of Computational and Experimental Science and Engineering, 11(3). https://doi.org/10.22399/ijcesen.3319

Issue

Section

Research Article