ADS-YOLO: A Multiscale Feature-Enhanced Network for Pepper Seedling Emergence Detection in Complex Plug-Tray Nursery Scenes

Accurate detection of emerged seedlings is essential for intelligent management in the context of industrialized pepper seeding cultivation. Manual assessment of seedling emergence rates is inefficient and subjective. Meanwhile, dense seedling distributions and complex substrate backgrounds in plug trays often cause false positives and missed detections in vision-based systems. To address these issues, Adaptive-Dynamic-Spatial YOLO (ADS-YOLO), a compact detection model, was built based upon YOLO11n. First, an Adaptive Scale Encoding (ASE) module was introduced into the neck to preserve fine-grained spatial information through parallel variable-scale processing and fusion. Second, a Dynamic Scale Sequence Fusion (DYSF) module combined content-aware DySample upsampling with a scale-indexed three-dimensional feature representation to improve multiscale alignment and fusion. Third, a Spatial-Channel Position Attention (SCPA) module was used to emphasize seedling-related responses and suppress interference from substrate particles, water stains, and reflections. On the pepper seedling validation set obtained from a fixed 8:1:1 image-level data split, ADS-YOLO achieved a precision of 79.0%, a recall of 75.5%, and an mAP@0.5 of 78.1%, representing improvements of 3.3, 3.2, and 2.9 percentage points over YOLO11n. The model contained approximately 3.04 M parameters and required 8.1 GFLOPs. Furthermore, a prototype based on offline video-frame analysis was developed to display seedling detections and frame-level counts. The prototype achieved an offline processing throughput of 24.5 FPS and a per-frame end-to-end processing time of 40.8 ms. These measurement results indicate that the method proposed in this paper can be used in practical scenarios, and also lay the foundation for future real-time tracking and counting.