Accurate classification of tree species is crucial for biodiversity conservation, forest resource management, and intelligent breeding. Traditional manual survey methods are inefficient and costly, while the AI-driven paradigm offers a new approach for intelligent forestry through computer vision technologies, enabling large-scale, high-precision tree species identification. However, fine-grained classification of tree species remains challenging due to high intra-class variation, low inter-class distinction, and complex environmental noise in real-world forest scenarios. To bridge the intra- and inter-class discrepancy, this study proposes a Forestry Fine-Grained Fusion Network (FFGF-Net), which integrates three key innovations: a CanopyPatchExtractor for local feature extraction, a CanopyTextureAnalyzer for multi-scale texture modeling, and a Cascaded Canopy Attention mechanism for noise suppression. For comprehensive evaluation, we introduce a newly collected UAV dataset, NJFUTreeData, and utilize established public datasets, including SZUTreeData and ETH dataset. Experimental results demonstrate that FFGF-Net achieves state-of-the-art performance, with Overall Accuracy (OA) of 75. 00% on NJFUTreeData and 94. 75% on the ETH dataset. Notably, our method consistently outperforms a wide range of existing model families, including representative CNN backbones (ConvNeXt-Tiny), lightweight architectures (e. g. , EfficientNet-B0, MobileNetV3-Small), and advanced Vision Transformers (e. g. , Swin-Tiny). The proposed network provides a robust technical foundation for automated forest resource surveys, ecological monitoring, and smart forestry applications. • Proposes FFGF-Net for complex fine-grained tree classification. • Achieves SOTA accuracy on both custom and public tree datasets. • Validates generality and robustness on other fine-grained benchmarks. • Achieves high efficiency to support edge deployment.
Zhou et al. (Tue,) studied this question.