Rapeseed seedling counting and geospatial localization system integrating visual tracking and real-time kinematic positioning

Abstract

Accurate estimation of rapeseed emergence requires reliable counting and spatially precise localization under field conditions. The video-based system, YOLO_DBSCAN_Track_RTK, integrates lightweight YOLOv11n detection, DeepSORT object tracking, Horizontal Adaptive Distance DBSCAN (HAD-DBSCAN) for crop-row clustering, a dynamic boundary-drift counting mechanism, and RTK-GNSS-aided georeferencing. Seedling centers are extracted frame by frame, clustered along the row direction with an adaptively estimated neighborhood radius and centroid-based inter-frame matching, then tracked and counted within a drifting spatiotemporal window. A calibrated projection chain links image coordinates to world coordinates by fusing visual trajectories with RTK reference points, thereby generating centimeter-level geospatial identities. Field experiments conducted on 12 videos covering 120 rapeseed varieties at the East Anhui Experimental Station of Anhui Agricultural University demonstrate strong performance: the detector achieves an AP of 93.6 % with a processing speed of 110 FPS; HAD-DBSCAN reaches 0.967 clustering accuracy while preserving row integrity under uneven density and delayed emergence; the tracking module attains a tracking accuracy (Pₜᵣ) of 92.5 %, a tracking precision (Pₘₜ) of 93.1 %, an ID switch rate (WID) of 7.4 %, and a counting precision (Pc) of 92.8 %; Geolocation yields a mean error of 2.84 cm with quasi-normal residuals centered near zero. These results establish a unified framework for efficient seedling counting and multi-temporal plant-level monitoring, enabling growth analysis to support high-throughput phenotyping.

Code and data availability

公開状態または取得可能な本文経路を確認できませんでした。

No evidence-backed public reproduction asset is currently recorded.