Wheat Head Detection in Field Environments Based on an Improved YOLOv11 Model

Yuting Zhang, Zihang Liu, Xiangdong Guo, Congcong Li () and Guifa Teng ()
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Yuting Zhang: College of Information Science and Technology, Hebei Agricultural University, Baoding 071001, China
Zihang Liu: College of Information Science and Technology, Hebei Agricultural University, Baoding 071001, China
Xiangdong Guo: School of Software, Shanxi Agricultural University, Taigu 030801, China
Congcong Li: College of Information Science and Technology, Hebei Agricultural University, Baoding 071001, China
Guifa Teng: College of Information Science and Technology, Hebei Agricultural University, Baoding 071001, China

Agriculture, 2025, vol. 15, issue 16, 1-19

Abstract: Precise wheat head detection is essential for plant counting and yield estimation in precision agriculture. To tackle the difficulties arising from densely packed wheat heads with diverse scales and intricate occlusions in real-world field conditions, this research introduces YOLO v11n-GRN, an improved wheat head detection model founded on the streamlined YOLO v11n framework. The model optimizes performance through three key innovations: This study introduces a Global Edge Information Transfer (GEIT) module architecture that incorporates a Multi-Scale Edge Information Generator (MSEIG) to enhance the perception of wheat head contours through effective modeling of edge features and deep semantic fusion. Additionally, a C3k2_RFCAConv module is developed to improve spatial awareness and multi-scale feature representation by integrating receptive field augmentation and a coordinate attention mechanism. The utilization of the Normalized Gaussian Wasserstein Distance (NWD) as the localization loss function enhances regression stability for distant small targets. Experiments were, respectively, validated on the self-built multi-temporal wheat field image dataset and the GWHD2021 public dataset. Results showed that, while maintaining a lightweight design (3.6 MB, 10.3 GFLOPs), the YOLOv11n-GRN model achieved a precision, recall, and mAP@0.5 of 92.5%, 91.1%, and 95.7%, respectively, on the self-built dataset, and 91.6%, 89.7%, and 94.4%, respectively, on the GWHD2021 dataset. This fully demonstrates that the improvements can effectively enhance the model’s comprehensive detection performance for wheat ear targets in complex backgrounds. Meanwhile, this study offers an effective technical approach for wheat head detection and yield estimation in challenging field conditions, showcasing promising practical implications.

Keywords: wheat head detection; enhanced YOLO v11n; attention mechanism; edge information modeling; small-target localization (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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