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Soleimanipour, Alireza, Rezaei Asl, Abbas, Shamloo, Roghaieh. (1404). Weeds detection in saffron fields using an improved YOLOv5 model. , 1(1), 44-50. doi: 10.22069/bere.2025.23036.1010
Alireza Soleimanipour; Abbas Rezaei Asl; Roghaieh Shamloo. "Weeds detection in saffron fields using an improved YOLOv5 model". , 1, 1, 1404, 44-50. doi: 10.22069/bere.2025.23036.1010
Soleimanipour, Alireza, Rezaei Asl, Abbas, Shamloo, Roghaieh. (1404). 'Weeds detection in saffron fields using an improved YOLOv5 model', , 1(1), pp. 44-50. doi: 10.22069/bere.2025.23036.1010
Soleimanipour, Alireza, Rezaei Asl, Abbas, Shamloo, Roghaieh. Weeds detection in saffron fields using an improved YOLOv5 model. , 1404; 1(1): 44-50. doi: 10.22069/bere.2025.23036.1010

Weeds detection in saffron fields using an improved YOLOv5 model

Biosystems Engineering and Renewable Energies
دوره 1، شماره 1، تیر 2025، صفحه 44-50    اصل مقاله (807.79 K)
نوع مقاله: Original Article
شناسه دیجیتال (DOI): 10.22069/bere.2025.23036.1010
نویسندگان
Alireza Soleimanipour* 1؛ Abbas Rezaei Asl2؛ Roghaieh Shamloo2
1Department of Biosystem Engineering, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan,
2Department of Biosystem Engineering, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, Iran
چکیده
The excessive use of agricultural pesticides and inputs has caused severe environmental damages to agricultural ecosystems. By applying digital agriculture and variable rate application systems, different sections of a farm can be managed with varying levels of pesticides and inputs, which is beneficial both in terms of production costs and environmental issues. In this study, a weed and saffron plant detection model was designed and evaluated to develop a selective weed control system in saffron fields. The proposed weed detection model is based on the YOLOv5 object detection model. Specifically, several CBS and C3 modules in the YOLOv5s model were replaced with Ghost Bottleneck and C3Ghost modules, respectively. This was done to reduce the number of model parameters and make the network lighter, which increases the speed of image processing during model training and inference. Furthermore, to improve the detection accuracy of the proposed model, a coordinate attention (CoordAtt) layer was used. The results showed that the number of parameters in the proposed model was reduced by 47% compared to the corresponding model in terms of network width and depth coefficients in YOLOv5 versions. Meanwhile, among the six trained models, the modified Yolov5s model demonstrated the best performance, achieving 81% accuracy and 67% recall. The detection accuracy of the proposed model was 3.93% higher than that of the best-performing YOLOv5 algorithm. Due to the lightweight nature of the proposed algorithm, it can be used for real-time weed detection in agricultural fields to develop selective control systems.

تازه های تحقیق

Research Highlights:

  • A novel weed detection model based on YOLOv5 is proposed to improve efficiency and accuracy in saffron fields.
  • The model utilizes innovative techniques like Ghost Bottleneck and C3Ghost modules to reduce computational complexity and enhance speed.
  • The incorporation of a coordinate attention layer significantly improves the model's ability to accurately detect weeds in challenging field conditions.
  • The proposed model offers a promising solution for selective weed control, leading to reduced pesticide usage and environmental impact.
کلیدواژه‌ها
Saffron؛ Weeds control؛ Object detection؛ Deep learning؛ YOLOv5
مراجع
Abdullaev, F. I. & Frenkel, G. D. (1999). Saffron in biological and medical research. Saffron: Crocus Sativus L, 103–113.

Adhinata, F. D. & Sumiharto, R. (2024). A comprehensive survey on weed and crop classification using machine learning and deep learning. Artificial Intelligence in Agriculture, 13, 45-63. https://doi.org/10.1016/j.aiia.2024.06.005

Admasu, G., Haji, J., Siyum, C., & Ndemo, E. (2024). Impact of Climate Change Adaptation Strategies on Food Security of Farm Households in Rural Dire Dawa Administration, Ethiopia. Sustainable Agriculture Research, 13(2), 1-15. https://doi.org/10.5539/sar.v13n2p1

Alif, M. A. R. & Hussain, M. (2024). YOLOv1 to YOLOv10: A comprehensive review of YOLO variants and their application in the agricultural domain. ArXiv Preprint. https://doi.org/10.48550/arXiv.2406.10139

Cheng, B., & Matson, E. T. (2015). A feature-based machine learning agent for automatic rice and weed discrimination. International Conference on Artificial Intelligence and Soft Computing, 517–527.

Fernández, J.-A. (2004). Biology, biotechnology and biomedicine of saffron.

Guerrero, J. M., Ruz, J. J. & Pajares, G. (2017). Crop rows and weeds detection in maize fields applying a computer vision system based on geometry. Computers and Electronics in Agriculture, 142, 461–472. https://doi.org/10.1016/j.compag.2017.09.028

Hamuda, E., Glavin, M., & Jones, E. (2016). A survey of image processing techniques for plant extraction and segmentation in the field. Computers and Electronics in Agriculture, 125, 184–199. https://doi.org/10.1016/j.compag.2016.04.024

Han, K., Wang, Y., Tian, Q., Guo, J., Xu, C., & Xu, C. (2020). Ghostnet: More features from cheap operations. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 1580–1589. https://doi.org/10.1109/CVPR42600.2020.00165

Hou, Q., Zhou, D., & Feng, J. (2021). Coordinate attention for efficient mobile network design. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 13713–13722. https://doi.org/10.1109/CVPR46437.2021.01350

Jeanmart, S., Edmunds, A. J. F., Lamberth, C., & Pouliot, M. (2016). Synthetic approaches to the 2010–2014 new agrochemicals. Bioorganic & Medicinal Chemistry, 24(3), 317–341.https://doi.org/10.1016/j.bmc.2015.12.014

José Bagur, M., Alonso Salinas, G. L., Jiménez-Monreal, A. M., Chaouqi, S., Llorens, S., Martínez-Tomé, M., & Alonso, G. L. (2017). Saffron: An old medicinal plant and a potential novel functional food. Molecules, 23(1), 30. https://doi.org/10.3390/molecules23010030

Kumar, P., & Misra, U. (2024). Deep Learning for Weed Detection: Exploring YOLO V8 Algorithm’s Performance in Agricultural Environments. 2024 2nd International Conference on Disruptive Technologies (ICDT), 255–258.       https://doi.org/10.1109/ICDT61202.2024.10489628

Kumar, R., Singh, V., Devi, K., Sharma, M., Singh, M. K., & Ahuja, P. S. (2008). State of art of saffron (Crocus sativus L.) agronomy: A comprehensive review. Food Reviews International, 25(1), 44–85. https://doi.org/10.1080/87559120802458503

Ministry of Agriculture-Jihad. (2020). Agricultural Statistics, (Vol. II). The Islamic Republic of Iran, Ministry of Agriculture-Jihad, Press. (In Persian).

Narayana, C. L., & Ramana, K. V. (2023). An efficient real-time weed detection technique using YOLOv7. International Journal of Advanced Computer Science and Applications, 14(2).https://doi.org/10.14569/ijacsa.2023.0140265

Peng, M., Zhang, W., Li, F., Xue, Q., Yuan, J., & An, P. (2023). Weed detection with improved YOLO v7. EAI Endorsed Transactions on Internet of Things, 9(3), e1.  https://doi.org/10.4108/eetiot.v9i3.3468

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, 779-788. https://doi.org/10.1109/CVPR.2016.91

Rodrigo, M. A., Oturan, N., & Oturan, M. A. (2014). Electrochemically assisted remediation of pesticides in soils and water: a review. Chemical Reviews, 114(17), 8720–8745. https://doi.org/10.1021/cr500077e

Shahnoushi, N., Abolhassani, L., Kavakebi, V., Reed, M., & Saghaian, S. (2020). Economic analysis of saffron production. In Saffron (pp. 337–356). Elsevier.https://doi.org/10.1016/B978-0-12-818638-1.00021-6

Singh, A., Ganapathysubramanian, B., Singh, A. K., & Sarkar, S. (2016). Machine learning for high-throughput stress phenotyping in plants. Trends in Plant Science, 21(2), 110–124. https://doi.org/10.1016/j.tplants.2015.10.015

Sonawane, S., & Patil, N. N. (2024). Deep learning-based weed detection in sesame crops using modified YOLOv5 model. Indian Journal of Weed Science, 56, 194-199.http://doi.org/10.5958/0974-8164.2024.00031.X

Woyessa, D. (2022). Weed control methods used in agriculture. American Journal of Life Science and Innovation, 1(1), 19–26. https://doi.org/10.54536/ajlsi.v1i1.413

Zhou, X., Chen, T., & Zhang, B. (2023). Research on the impact of digital agriculture development on agricultural green total factor productivity. Land, 12(1), 195. https://doi.org/10.3390/land12010195

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