YOLOv8n-DSP: A High-Precision Model for Oat Ear Detection and Counting in Complex Fields

Accurate detection and counting of oat ears are essential for yield estimation but remain challenging in complex field environments due to occlusion, significant scale variation, and fluctuating lighting. The aim of this study is to develop a high-precision detection and counting model to address these challenges. The adopted methodology was an improved YOLOv8n model, named YOLOv8n-DSP. To address significant scale variation, a Diverse Branch Block (DBB) was introduced into the backbone to enhance multi-scale feature representation. For improved detection of small, dense oat ears, the neck was augmented with a Spatial and Channel Synergistic Attention (SCSA) mechanism to strengthen discriminative feature extraction. Furthermore, to refine localization among overlapping oat ears, the PIoUv2 loss function was employed for bounding box regression. The main results revealed that the proposed model achieved a mean average precision (mAP) of 94.0% and an F1-score of 87.1% on the oat ear detection task, representing gains of 3.2 and 1.8 percentage points over the baseline YOLOv8n, respectively. For counting, it reached an accuracy of 82.5%, a 9.2-point improvement. In conclusion, the YOLOv8n-DSP model provides an effective and practical approach for in-field oat ear detection and counting, suggesting considerable potential as a reliable tool for future yield prediction systems and advanced intelligent agricultural equipment.