A Two-Stage Segment-Then-Classify Strategy for Accurate Ginkgo Tree Identification from UAV Imagery

Ginkgo biloba L. plays an important role in biodiversity conservation. Accurate identification of Ginkgo in forest environments remains challenging due to its visual similarity to other broad-leaved species during the green-leaf period and to species with yellow foliage during autumn. In this study, we propose a novel two-stage segment-then-classify (STC) strategy to improve the accuracy of Ginkgo identification from unmanned aerial vehicle (UAV) imagery. First, the Segment Anything Model (SAM) was fine-tuned for canopy segmentation across the green-leaf stage and the yellow-leaf stage. A post-processing pipeline was developed to optimize mask quality, ensuring independent and complete tree crown segmentation. Subsequently, a ResNet-101-based classification model was trained to distinguish Ginkgo from other tree species. The experimental results showed that the STC strategy achieved significant improvements compared to the YOLOv8 model. In the yellow-leaf stage, it reached an F1-score of 92.96%, improving by 24.50 percentage points over YOLOv8. In the more challenging green-leaf stage, the F1-score improved by 31.27 percentage points, surpassing YOLOv8’s best performance in the yellow-leaf stage. These findings demonstrate that the STC framework provides a reliable solution for high-precision identification of Ginkgo in forest ecosystems, offering valuable support for biodiversity monitoring and forest management.