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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
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