Investigating the Potential of the Innovative YOLOv8s Model for Detecting Bloomed Damask Roses in Open Fields

Document Type : Research Article-en

Authors

• F. Fatehi
• H. Bagherpour
• J. Amiri Parian

Department of Biosystems Engineering, Faculty of Agriculture, Bu-Ali Sina University, Hamedan, Iran

https://doi.org/10.22067/jam.2024.88066.1249

Abstract

Manually picking the flowers of the Damask rose is significantly challenging due to the numerous thorns on its stems. Consequently, the accurate detection of bloomed Damask roses in open fields is crucial for designing a robot capable of automating the harvesting process. Considering the high speed and precise capabilities of deep convolutional neural networks (DCNN), the objective of this study is to investigate the effectiveness of the optimized YOLOv8s model in detecting bloomed Damask roses. To assess the impact of the YOLO model size on network performance, the precision and detection speed of other YOLO network versions, including v5s and v6s, were also examined. Images of Damask roses were taken under two lighting conditions: normal light conditions (from civil twilight to sunrise) and intense light conditions (from sunrise to 10 AM). The outcomes demonstrated that YOLOv8s exhibited the highest performance, with a mean average precision (mAP50) of 98% and a detection speed of 243.9 fps. This outperformed the mAP50 and detection speed of YOLOv5s and YOLOv6s networks by margins of 0.3%, 6.1%, 169.3 fps and 198.6 fps, respectively. Experimental results show that YOLOv8s performs better on images taken in normal lighting than on those taken in intense lighting. A decline of 5.2% in mAP50 and 2.4% in detection speed signifies the adverse influence of intense ambient light on the model's effectiveness. This research indicates that the real-time detector YOLOv8s provides a feasible solution for the identification of Damask rose and provides guidance for the detection of other similar plants.

Keywords

• Ambient light
• Deep learning
• Object detection
• Rose flower
• YOLO

Main Subjects

• Precision Farming

©2025 The author(s). This is an open access article distributed under Creative Commons Attribution 4.0 International License (CC BY 4.0)

References

1. Anjani, I. A., Pratiwi, Y. R., & Nurhuda, S. N. B. (2021, March). Implementation of deep learning using convolutional neural network algorithm for classification rose flower. In Journal of Physics: Conference Series (Vol. 1842, No. 1, p. 012002). IOP Publishing. https://doi.org/10.1088/1742-6596/1842/1/012002
2. Apeinans, I., Sondors, M., Litavniece, L., Kodors, S., Zarembo, I., & Feldmane, D. (2024, June). Cherry Fruitlet Detection using YOLOv5 or YOLOv8? In  TECHNOLOGIES. RESOURCES. Proceedings of the International Scientific and Practical Conference(Vol. 2, pp. 29-33). https://doi.org/10.17770/etr2024vol2.8013
3. Bataduwaarachchi, S. D., Sattarzadeh, A. R., Stewart, M., Ashcroft, B., Morrison, A., & North, S. (2023). Towards autonomous cross-pollination: Portable multi-classification system for in situ growth monitoring of tomato flowers. Smart Agricultural Technology, 4, 100205. https://doi.org/10.1016/j.atech.2023.100205
4. 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
5. Estrada, J. S., Vasconez, J. P., Fu, L., & Cheein, F. A. (2024). Deep Learning based flower detection and counting in highly populated images: A peach grove case study. Journal of Agriculture and Food Research, 15, 100930. https://doi.org/10.1016/j.jafr.2023.100930
6. Guru, D. S., Kumar, Y. S., & Manjunath, S. (2011). Textural features in flower classification. Mathematical and Computer Modelling, 54(3-4), 1030-1036. https://doi.org/10.1016/j.mcm.2010.11.032
7. Jocher, G., Chaurasia, A., & Qiu, J. (2023). YOLO by Ultralytics. https://github.com/ultralytics/ultralytics
8. Kumar, R., Sharma, S., Sood, S., Agnihotri, V. K., & Singh, B. (2013). Effect of diurnal variability and storage conditions on essential oil content and quality of damask rose (Rosa damascena) flowers in north western Himalayas. Scientia Horticulturae, 154, 102-108. https://doi.org/10.1016/j.scienta.2013.02.002
9. Manikanta, Y. E. R. R. A. P. O. T. H. U., Rao, S. S., & Venkatesh, R. (2017). The design and simulation of rose harvesting robot. International Journal of Mechanical and Production Engineering Research and Development, 9(1), 191-200.
10. Mithra, S., & Nagamalleswari, T. Y. J. (2023). Cucurbitaceous family flower inferencing using deep transfer learning approaches: CuCuFlower UAV imagery data. Soft Computing, 27(12), 8345-8356. https://doi.org/10.1007/s00500-023-08186-w
11. Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2016). You only look once: Unified, real-time object detection. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 779-788).
12. Rusanov, K., Kovacheva, N., Rusanova, M., & Atanassov, I. (2011). Traditional Rosa damascena flower harvesting practices evaluated through GC/MS metabolite profiling of flower volatiles. Food Chemistry, 129(4), 1851-1859. https://doi.org/1016/j.foodchem.2011.05.132
13. Sharma, S., & Kumar, R. (2018). Influence of Harvesting Stage and Distillation Time of Damask Rose (Rosa damascena) Flowers on Essential Oil Content and Composition in the Western Himalayas. Journal of Essential Oil-Bearing Plants, 21(1), 92-102. https://doi.org/10.1080/0972060X.2017.1399089
14. Shinoda, R., Motoki, K., Hara, K., Kataoka, H., Nakano, R., Nakazaki, T., & Noguchi, R. (2023). RoseTracker: a system for automated rose growth monitoring. Smart Agricultural Technology, 100271. https://doi.org/10.1016/j.atech.2023.100271
15. Silva, G., Monteiro, R., Ferreira, A., Carvalho, P., & Corte-Real, L. (2019). Face detection in thermal images with YOLOv3. In Advances in Visual Computing: 14th International Symposium on Visual Computing, ISVC 2019, Lake Tahoe, NV, USA, October 7–9, 2019, Proceedings, Part II 14(pp. 89-99). Springer International Publishing. https://doi.org/10.1007/978-3-030-33723-0_8
16. Sun, K., Wang, X., Liu, S., & Liu, C. (2021). Apple, peach, and pear flower detection using semantic segmentation network and shape constraint level set. Computers and Electronics in Agriculture, 185, 106150. https://doi.org/10.1016/j.compag.2021.106150
17. Terven, J., & Cordova-Esparza, D. (2023). A comprehensive review of YOLO: From YOLOv1 to YOLOv8 and beyond. arXiv preprint arXiv:2304.00501. https://doi.org/10.3390/make5040083
18. Thakur, M., Sharma, S., Sharma, U., & Kumar, R. (2019). Study on effect of pruning time on growth, yield and quality of scented rose (Rosa damascena) varieties under acidic conditions of western Himalayas. Journal of Applied Research on Medicinal and Aromatic Plants, 13, 100202. https://doi.org/10.1016/j.jarmap.2019.100202
19. Tung, C., Kelleher, M. R., Schlueter, R. J., Xu, B., Lu, Y. H., Thiruvathukal, G. K., ... & Lu, Y. (2019, March). Large-scale object detection of images from network cameras in variable ambient lighting conditions. In 2019 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR) (pp. 393-398). https://doi.org/10.1109/MIPR.2019.00080
20. Ucar, Y., Kazaz, S., Eraslan, F., & Baydar, H. (2017). Effects of different irrigation water and nitrogen levels on the water use, rose flower yield and oil yield of Rosa damascena. Agricultural Water Management, 182, 94-102. https://doi.org/10.1016/j.agwat.2016.12.004
21. Ultralytics. (n.d.). YOLOv8: Model architecture. Retrieved from https://docs.ultralytics.com/models/yolov8/
22. Wang, C., Wang, Y., Liu, S., Lin, G., He, P., Zhang, Z., & Zhou, Y. (2022). Study on pear flowers detection performance of YOLO-PEFL model trained with synthetic target images. Frontiers in Plant Science, 13. https://doi.org/10.3389/fpls.2022.911473
23. Wang, Z., Underwood, J., & Walsh, K. B. (2018). Machine vision assessment of mango orchard flowering. Computers and Electronics in Agriculture, 151, 501-511. https://doi.org/10.1016/j.compag.2018.06.040
24. Wang, D., & He, D. (2021). Channel pruned YOLO V5s-based deep learning approach for rapid and accurate apple fruitlet detection before fruit thinning. Biosystems Engineering, 210, 271-281. https://doi.org/10.1016/j.biosystemseng.2021.08.015
25. Wu, D., Lv, S., Jiang, M., & Song, H. (2020). Using channel pruning-based YOLO v4 deep learning algorithm for the real-time and accurate detection of apple flowers in natural environments. Computers and Electronics in Agriculture, 178, 105742. https://doi.org/1016/j.compag.2020.105742
26. Yousefi, B., & Jaimand, K. (2018). Chemical variation in the essential oil of Iranian Rosa damascena landraces under semi-arid and cool conditions. International Journal of Horticultural Science and Technology, 5(1), 81-92. https://doi.org/10.22059/ijhst.2018.256329.234
27. Zhang, M., Su, H., & Wen, J. (2021). Classification of flower image based on attention mechanism and multi-loss attention network. Computer Communications, 179, 307-317. https://doi.org/10.1016/j.comcom.2021.09.001