ARCHIVES
Original Article
Development and Validation of a Real-Time YOLOv4-Based Multi-Fruit Detection Model for Autonomous Robotic Harvesting
Paul Nyamwange Ombuna1
Department of Computing and Mathematics, School of Computing, Co-operative University of Kenya, Nairobi, Kenya.
Published Online: September-December 2025
Pages: 89-93
Cite this article
↗ https://www.doi.org/10.59256/indjcst.20250403017
References
1. FAO. (2017). The future of food and agriculture: Trends and challenges. Food and Agriculture Organization of the United Nations. http://www.fao.org/3/i6583e/i6583e.pdf
2. Bogue, R. (2021). Robots in agriculture: Prospects and challenges. Industrial Robot, 48(4), 524-530. https://doi.org/10.1108/IR-02-2021-0041
3. Kamilaris, A., & Prenafeta-Boldú, F. X. (2018). Deep learning in agriculture: A survey. Computers and Electronics in Agriculture, 147, 70-90. https://doi.org/10.1016/j.compag.2018.02.016
4. Redmon, J., & Farhadi, A. (2018). YOLOv3: An incremental improvement. arXiv preprint. https://arxiv.org/abs/1804.02767
5. Bochkovskiy, A., Wang, C. Y., & Liao, H. Y. M. (2020). YOLOv4: Optimal speed and accuracy of object detection. arXiv preprint. https://arxiv.org/abs/2004.10934
6. Kurtulmus, F., Lee, W. S., & Vardar, A. (2014). Green citrus detection using 'eigenfruit', color and circular Gabor texture features. Computers and Electronics in Agriculture, 106, 178-186. https://doi.org/10.1016/j.compag.2014.05.011
7. Bargoti, S., & Underwood, J. (2017). Deep fruit detection in orchards. IEEE Robotics and Automation Letters, 2(2), 902-909. https://doi.org/10.1109/LRA.2017.2651944
8. Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C. Y., & Berg, A. C. (2016). SSD: Single shot multibox detector. European Conference on Computer Vision, 21-37. https://doi.org/10.1007/978-3-319-46448-0_2
9. Chen, J., Liu, H., Zhang, Y., Zhang, D., & Chen, X. (2021). A YOLOv4-based apple detection method for harvesting robots. Precision Agriculture, 22(4), 1147-1165. https://doi.org/10.1007/s11119-020-09774-8
10. Sa, I., Ge, Z., Dayoub, F., Upcroft, B., Perez, T., & McCool, C. (2016). DeepFruits: A fruit detection system using deep neural networks. Sensors, 16(8), 1222. https://doi.org/10.3390/s16081222
11. Shamshiri, R. R., Weltzien, C., Hameed, I. A., Yule, I. J., Grift, T. E., et al. (2018). Research and development in agricultural robotics: A perspective of digital farming. International Journal of Agricultural and Biological Engineering, 11(4), 1-14. https://doi.org/10.25165/j.ijabe.20181104.4278
12. Bechar, A., & Vigneault, C. (2017). Agricultural robots for field operations. Biosystems Engineering, 153, 110-125. https://doi.org/10.1016/j.biosystemseng.2016.11.004
13. Ren, S., He, K., Girshick, R., & Sun, J. (2017). Faster R-CNN: Towards real-time object detection with region proposal networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(6), 1137-1149. https://doi.org/10.1109/TPAMI.2016.2577031
14. Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2016). You only look once: Unified, real-time object detection. IEEE Conference on Computer Vision and Pattern Recognition, 779-788. https://doi.org/10.1109/CVPR.2016.91
15. Koirala, A., Walsh, K. B., Wang, Z., & McCarthy, C. (2019). Deep learning for real-time fruit detection and orchard fruit load estimation. Precision Agriculture, 20(6), 1107-1135. https://doi.org/10.1007/s11119-019-09642-0
16. Kuznetsova, A., Rom, H., Alldrin, N., Uijlings, J., Krasin, I., et al. (2020). The Open Images Dataset V4. International Journal of Computer Vision, 128(7), 1956-1981. https://doi.org/10.1007/s11263-020-01316-z
17. Wang, M., & Deng, W. (2018). Deep visual domain adaptation: A survey. Neurocomputing, 312, 135-153. https://doi.org/10.1016/j.neucom.2018.05.083
18. Gongal, A., Amatya, S., Karkee, M., Zhang, Q., & Lewis, K. (2015). Sensors and systems for fruit detection and localization. Computers and Electronics in Agriculture, 116, 8-19. https://doi.org/10.1016/j.compag.2015.05.021
19. Choi, D., Chun, D., Kim, H., & Lee, H. J. (2019). Gaussian YOLOv3: An accurate and fast object detector using localization uncertainty. IEEE Access, 7, 56833-56843. https://doi.org/10.1109/ACCESS.2019.2913832
20. Li, B., Liu, Y., & Wang, X. (2019). Gradient harmonized single-stage detector. AAAI Conference on Artificial Intelligence, 33(1), 8577-8584. https://doi.org/10.1609/aaai.v33i01.33018577
21. Fu, L., Feng, Y., Majeed, Y., Zhang, X., Zhang, J., Karkee, M., & Zhang, Q. (2018). Kiwifruit detection in field images using Faster R-CNN with ZFNet. IFAC-PapersOnLine, 51(17), 45-50. https://doi.org/10.1016/j.ifacol.2018.08.059
22. Zhang, Z., Peng, H., & Wang, J. (2019). Real-time object detection for smart vehicles. IEEE Conference on Computer Vision and Pattern Recognition, 8418-8427. https://doi.org/10.1109/CVPR.2019.00861
23. Huang, J., Rathod, V., Sun, C., Zhu, M., Korattikara, A., et al. (2017). Speed/accuracy trade-offs for modern convolutional object detectors. IEEE Conference on Computer Vision and Pattern Recognition, 7310-7311. https://doi.org/10.1109/CVPR.2017.351
24. Rahnemoonfar, M., & Sheppard, C. (2017). Deep count: Fruit counting based on deep simulated learning. Sensors, 17(4), 905. https://doi.org/10.3390/s17040905
25. Dias, P. A., Tabb, A., & Medeiros, H. (2018). Apple flower detection using deep convolutional networks. Computers in Industry, 99, 17-28. https://doi.org/10.1016/j.compind.2018.03.010
26. Zhao, Y., Gong, L., Huang, Y., & Liu, C. (2016). A review of key techniques of vision-based control for harvesting robot. Computers and Electronics in Agriculture, 127, 311-323. https://doi.org/10.1016/j.compag.2016.06.022
2. Bogue, R. (2021). Robots in agriculture: Prospects and challenges. Industrial Robot, 48(4), 524-530. https://doi.org/10.1108/IR-02-2021-0041
3. Kamilaris, A., & Prenafeta-Boldú, F. X. (2018). Deep learning in agriculture: A survey. Computers and Electronics in Agriculture, 147, 70-90. https://doi.org/10.1016/j.compag.2018.02.016
4. Redmon, J., & Farhadi, A. (2018). YOLOv3: An incremental improvement. arXiv preprint. https://arxiv.org/abs/1804.02767
5. Bochkovskiy, A., Wang, C. Y., & Liao, H. Y. M. (2020). YOLOv4: Optimal speed and accuracy of object detection. arXiv preprint. https://arxiv.org/abs/2004.10934
6. Kurtulmus, F., Lee, W. S., & Vardar, A. (2014). Green citrus detection using 'eigenfruit', color and circular Gabor texture features. Computers and Electronics in Agriculture, 106, 178-186. https://doi.org/10.1016/j.compag.2014.05.011
7. Bargoti, S., & Underwood, J. (2017). Deep fruit detection in orchards. IEEE Robotics and Automation Letters, 2(2), 902-909. https://doi.org/10.1109/LRA.2017.2651944
8. Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C. Y., & Berg, A. C. (2016). SSD: Single shot multibox detector. European Conference on Computer Vision, 21-37. https://doi.org/10.1007/978-3-319-46448-0_2
9. Chen, J., Liu, H., Zhang, Y., Zhang, D., & Chen, X. (2021). A YOLOv4-based apple detection method for harvesting robots. Precision Agriculture, 22(4), 1147-1165. https://doi.org/10.1007/s11119-020-09774-8
10. Sa, I., Ge, Z., Dayoub, F., Upcroft, B., Perez, T., & McCool, C. (2016). DeepFruits: A fruit detection system using deep neural networks. Sensors, 16(8), 1222. https://doi.org/10.3390/s16081222
11. Shamshiri, R. R., Weltzien, C., Hameed, I. A., Yule, I. J., Grift, T. E., et al. (2018). Research and development in agricultural robotics: A perspective of digital farming. International Journal of Agricultural and Biological Engineering, 11(4), 1-14. https://doi.org/10.25165/j.ijabe.20181104.4278
12. Bechar, A., & Vigneault, C. (2017). Agricultural robots for field operations. Biosystems Engineering, 153, 110-125. https://doi.org/10.1016/j.biosystemseng.2016.11.004
13. Ren, S., He, K., Girshick, R., & Sun, J. (2017). Faster R-CNN: Towards real-time object detection with region proposal networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(6), 1137-1149. https://doi.org/10.1109/TPAMI.2016.2577031
14. Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2016). You only look once: Unified, real-time object detection. IEEE Conference on Computer Vision and Pattern Recognition, 779-788. https://doi.org/10.1109/CVPR.2016.91
15. Koirala, A., Walsh, K. B., Wang, Z., & McCarthy, C. (2019). Deep learning for real-time fruit detection and orchard fruit load estimation. Precision Agriculture, 20(6), 1107-1135. https://doi.org/10.1007/s11119-019-09642-0
16. Kuznetsova, A., Rom, H., Alldrin, N., Uijlings, J., Krasin, I., et al. (2020). The Open Images Dataset V4. International Journal of Computer Vision, 128(7), 1956-1981. https://doi.org/10.1007/s11263-020-01316-z
17. Wang, M., & Deng, W. (2018). Deep visual domain adaptation: A survey. Neurocomputing, 312, 135-153. https://doi.org/10.1016/j.neucom.2018.05.083
18. Gongal, A., Amatya, S., Karkee, M., Zhang, Q., & Lewis, K. (2015). Sensors and systems for fruit detection and localization. Computers and Electronics in Agriculture, 116, 8-19. https://doi.org/10.1016/j.compag.2015.05.021
19. Choi, D., Chun, D., Kim, H., & Lee, H. J. (2019). Gaussian YOLOv3: An accurate and fast object detector using localization uncertainty. IEEE Access, 7, 56833-56843. https://doi.org/10.1109/ACCESS.2019.2913832
20. Li, B., Liu, Y., & Wang, X. (2019). Gradient harmonized single-stage detector. AAAI Conference on Artificial Intelligence, 33(1), 8577-8584. https://doi.org/10.1609/aaai.v33i01.33018577
21. Fu, L., Feng, Y., Majeed, Y., Zhang, X., Zhang, J., Karkee, M., & Zhang, Q. (2018). Kiwifruit detection in field images using Faster R-CNN with ZFNet. IFAC-PapersOnLine, 51(17), 45-50. https://doi.org/10.1016/j.ifacol.2018.08.059
22. Zhang, Z., Peng, H., & Wang, J. (2019). Real-time object detection for smart vehicles. IEEE Conference on Computer Vision and Pattern Recognition, 8418-8427. https://doi.org/10.1109/CVPR.2019.00861
23. Huang, J., Rathod, V., Sun, C., Zhu, M., Korattikara, A., et al. (2017). Speed/accuracy trade-offs for modern convolutional object detectors. IEEE Conference on Computer Vision and Pattern Recognition, 7310-7311. https://doi.org/10.1109/CVPR.2017.351
24. Rahnemoonfar, M., & Sheppard, C. (2017). Deep count: Fruit counting based on deep simulated learning. Sensors, 17(4), 905. https://doi.org/10.3390/s17040905
25. Dias, P. A., Tabb, A., & Medeiros, H. (2018). Apple flower detection using deep convolutional networks. Computers in Industry, 99, 17-28. https://doi.org/10.1016/j.compind.2018.03.010
26. Zhao, Y., Gong, L., Huang, Y., & Liu, C. (2016). A review of key techniques of vision-based control for harvesting robot. Computers and Electronics in Agriculture, 127, 311-323. https://doi.org/10.1016/j.compag.2016.06.022
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