MCA-YOLO: An Improved YOLOv11n-Based Model for Precise Detection of Cotton Apical Buds

Precise detection of cotton apical buds is the primary step toward achieving intelligent topping operations. Existing object detection models still struggle to accurately recognize dense small targets under complex field conditions. In this study, we propose an improved model, MCA-YOLO, based on YOLOv11n, and optimize it from three aspects: feature extraction, computational efficiency, and multi-scale feature fusion. First, we introduce the MLCA attention mechanism into the PSABlock to construct the C2PSA_MLCA module, enhancing the model’s capability to represent both local and global features. Second, a CSPHet module is reconstructed using heterogeneous convolution (HetConv) combined with a dual-path design to reduce convolutional redundancy and improve feature extraction efficiency. Finally, the original YOLOv11n detection head is replaced with an ASFFHead, enabling adaptive multi-scale feature fusion, thereby improving detection performance for small, dense, and scale-varying targets. Experimental results show that MCA-YOLO achieves Precision, Recall, [email protected], and F1-score of 89.0%, 83.1%, 90.6%, and 85.9%, corresponding to improvements of 3.0, 8.1, 7.1, and 5.8 percentage points over YOLOv11n. Compared with YOLOv11n, the parameters and GFLOPs increase by 50.0% and 31.7%. Even with this increase in model complexity, MCA-YOLO achieves 75 FPS with a model size of 7.76 MB, indicating that it maintains real-time detection capability while improving detection accuracy.