آمار
| تعداد نشریات | 13 |
| تعداد شمارهها | 639 |
| تعداد مقالات | 6,654 |
| تعداد مشاهده مقاله | 9,082,085 |
| تعداد دریافت فایل اصل مقاله | 8,561,484 |
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:
| ||
| 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 | ||
|
آمار تعداد مشاهده مقاله: 75 تعداد دریافت فایل اصل مقاله: 38 |
||
سامانه مدیریت نشریات علمی. طراحی و پیاده سازی از سیناوب