CSM-YOLO: An integrated lightweight framework for UAV-based detection and geospatial analysis of missing cotton seedlings

• Developed CSM-YOLO for high-precision cotton seedling detection and geolocation. • Integrated lightweight CA-StarNet and C3k2-RAB for improved small-object detection. • Proposed two coordinate-mapping strategies for accurate spatial localization. • Introduced graded visualization for assessing missing-seedling rates in fields. Accurate monitoring of cotton seedling establishment is essential for optimizing yield and field management in precision agriculture. Gaps caused by germination failure or uneven mechanical sowing can substantially reduce final yield and complicate subsequent field operations. This study aimed to develop a real-time, accurate, and scalable framework for monitoring missing cotton seedling establishment and quantifying missing-seedling gaps from UAV imagery. We propose CSM-YOLO, an end-to-end integrated framework comprising three core components: (i) a lightweight yet high-performance detection model (CSM-YOLO), in which the CA-StarNet backbone enables efficient feature encoding, the multi-scale C3k2-RAB module enhances fine-grained seedling representation, and the adaptive APT-TAL label assignment strategy improves feature–anchor matching accuracy, with the three components jointly achieving an optimal balance among accuracy, robustness, and inference efficiency; (ii) two complementary pixel-to-geographic coordinate mapping strategies, including a novel linear interpolation method and an enhanced collinearity equation method, each offering distinct advantages in terms of computational efficiency and georeferencing accuracy; and (iii) a regional grading and visualization module for missing-seedling rates that supports both quantitative assessment and spatial interpretation within a unified workflow. CSM-YOLO achieved a mean average precision ([email protected]) of 97.23%, with class-specific precisions of 97.87% for cotton seedlings and 96.89% for missing-seedling holes, significantly outperforming mainstream models (Faster R-CNN, SSD, YOLOv3-tiny, YOLOv5n, YOLOv8n, YOLOv10n, YOLOv11n, and YOLOv11s). The two coordinate-mapping strategies yielded mean localization errors of 0.155 m and 0.004 m, respectively, when validated against orthophoto reference points. The framework provides high-precision detection, efficient georeferencing, and intuitive visualizations of seedling missing patterns. It explores the feasibility of an integrated pipeline that links detection, localization, and visualization for UAV-based cotton field monitoring. By coupling these components into a coherent workflow, the proposed system enables high-throughput quality assessment, precise reseeding planning, and accurate identification of weed-infested zones, thereby offering a robust and scalable solution for data-driven decision-making in precision cotton production.