Improved YOLOv10n model for enhanced cotton recognition in complex environments - Yutao Gong; Wenwen Ding; Nenghui Huang; Tao Li; Juntao Zhou (Smart Agricultural Technology, 2025)
Accurate and efficient identification of cotton boll states is crucial for mechanical and intelligent cotton harvesting. Currently, target detection models struggle with multi - category cotton recognition in complex farmland environments. To address this, this study improves the YOLOv10n model and proposes an enhanced YOLOv10n - based cotton recognition model.Firstly, the SimAM attention mechanism is integrated into the backbone network to strengthen the model's ability to focus on key features of multi - category cotton. Secondly, partial convolution (PConv) replaces standard convolution in the C2f module, creating a lightweight C2f - PConv structure to reduce computational redundancy.Test results indicate that the improved YOLOv10n model achieves 94 % detection accuracy, 89.7 % recall, and 94.8 % mAP@0.5. Compared to YOLOv5, YOLOv7, YOLOv8, and the baseline YOLOv10n models, its mAP@0.5 increases by 6.3, 5.6, 3.9, and 1.3 percentage points, respectively. With 1.45 M parameters and 2.8 G computations, it represents a 5.8 % and 15.2 % reduction from the original YOLOv10n model.In complex farmland settings, the enhanced YOLOv10n model can precisely identify multi - category cotton growth stages, optimize network computational efficiency, and support the development of visual systems for cotton - harvesting robots.