Yolov8n-RCP: An Improved Algorithm for Small-Target Detection in Complex Crop Environments

Traditional methods for picking small-target crops like pepper are time-consuming, labor-intensive, and costly, whereas deep learning-based object detection algorithms can rapidly identify mature peppers and guide mechanical arms for automated picking. Aiming at the low detection accuracy of peppers in natural field environments (due to small target size and complex backgrounds), this study proposes an improved Yolov8n-based algorithm (named Yolov8n-RCP, where RCP stands for RVB-CA-Pepper) for accurate mature pepper detection. The acronym directly reflects the algorithm’s core design: integrating the Reverse Bottleneck (RVB) module for lightweight feature extraction and the Coordinate Attention (CA) mechanism for background noise suppression, dedicated to mature pepper detection in complex crop environments. Three key optimizations are implemented: (1) The proposed C2F_RVB module enhances the model’s comprehension of input positional structure while maintaining the same parameter count (3.46 M) as the baseline. By fusing RepViTBlocks (for structural reparameterization) and EMA multi-scale attention (for color feature optimization), it improves feature extraction efficiency—specifically, reducing small target-related redundant FLOPs by 18% and achieving a small-pepper edge IoU of 92% (evaluated via standard edge matching with ground-truth annotations)—thus avoiding the precision-complexity trade-off. (2) The feature extraction network is optimized to retain a lightweight architecture (suitable for real-time deployment) while boosting precision. (3) The Coordinate Attention (CA) mechanism is integrated into the feature extraction network to suppress low-level feature noise. Experimental results show that Yolov8n-RCP achieves 96.4% precision (P), 91.1% recall (R), 96.2% mAP0.5, 84.7% mAP0.5:0.95, and 90.74 FPS—representing increases of 3.5%, 6.1%, 4.4%, 8.1%, and 11.58FPS, respectively, compared to the Yolov8n baseline. With high detection precision and fast recognition speed, this method enables accurate mature pepper detection in natural environments, thereby providing technical support for electrically driven automated pepper-picking systems—a critical application scenario in agricultural electrification.