WED-YOLO: A Detection Model for Safflower Under Complex Unstructured Environment

Zhenguo Zhang (), Yunze Wang, Peng Xu, Ruimeng Shi, Zhenyu Xing and Junye Li
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Zhenguo Zhang: College of Mechanical and Electrical Engineering, Xinjiang Agricultural University, Urumqi 830052, China
Yunze Wang: College of Mechanical and Electrical Engineering, Xinjiang Agricultural University, Urumqi 830052, China
Peng Xu: College of Mechanical and Electrical Engineering, Xinjiang Agricultural University, Urumqi 830052, China
Ruimeng Shi: College of Mechanical and Electrical Engineering, Xinjiang Agricultural University, Urumqi 830052, China
Zhenyu Xing: College of Agriculture, Nanjing Agricultural University, Nanjing 210095, China
Junye Li: College of Mechanical and Electrical Engineering, Xinjiang Agricultural University, Urumqi 830052, China

Agriculture, 2025, vol. 15, issue 2, 1-19

Abstract: Accurate safflower recognition is a critical research challenge in the field of automated safflower harvesting. The growing environment of safflowers, including factors such as variable weather conditions in unstructured environments, shooting distances, and diverse morphological characteristics, presents significant difficulties for detection. To address these challenges and enable precise safflower target recognition in complex environments, this study proposes an improved safflower detection model, WED-YOLO, based on YOLOv8n. Firstly, the original bounding box loss function is replaced with the dynamic non-monotonic focusing mechanism Wise Intersection over Union (WIoU), which enhances the model’s bounding box fitting ability and accelerates network convergence. Then, the upsampling module in the network’s neck is substituted with the more efficient and versatile dynamic upsampling module, DySample, to improve the precision of feature map upsampling. Meanwhile, the EMA attention mechanism is integrated into the C2f module of the backbone network to strengthen the model’s feature extraction capabilities. Finally, a small-target detection layer is incorporated into the detection head, enabling the model to focus on small safflower targets. The model is trained and validated using a custom-built safflower dataset. The experimental results demonstrate that the improved model achieves Precision ( P ), Recall ( R ), mean Average Precision ( mAP ), and F 1 score values of 93.15%, 86.71%, 95.03%, and 89.64%, respectively. These results represent improvements of 2.9%, 6.69%, 4.5%, and 6.22% over the baseline model. Compared with Faster R-CNN, YOLOv5, YOLOv7, and YOLOv10, the WED-YOLO achieved the highest mAP value. It outperforms the module mentioned by 13.06%, 4.85%, 4.86%, and 4.82%, respectively. The enhanced model exhibits superior precision and lower miss detection rates in safflower recognition tasks, providing a robust algorithmic foundation for the intelligent harvesting of safflowers.

Keywords: safflower filaments; object detection; improved YOLOv8 algorithm; deep learning (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2025
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