Accurate fine-grained tree species classification is critical for forest inventory and biodiversity monitoring. Existing methods predominantly focus on designing complex architectures to fit local data distributions. However, they often overlook the long-tailed distributions and high inter-class similarity inherent in limited data, thereby struggling to distinguish between few-shot or confusing categories. In the process of knowledge dissemination in the human world, individuals will actively seek expert assistance to transcend the limitations of local thinking. Inspired by this, we introduce an external "Domain Expert" and propose an Expert Knowledge-Guided Classification Decision Calibration Network (EKDC-Net) to overcome these challenges. Our framework addresses two core issues: expert knowledge extraction and utilization. Specifically, we first develop a Local Prior Guided Knowledge Extraction Module (LPKEM). By leveraging Class Activation Map (CAM) analysis, LPKEM guides the domain expert to focus exclusively on discriminative features essential for classification. Subsequently, to effectively integrate this knowledge, we design an Uncertainty-Guided Decision Calibration Module (UDCM). This module dynamically corrects the local model's decisions by considering both overall category uncertainty and instance-level prediction uncertainty. Furthermore, we present a large-scale classification dataset covering 102 tree species, named CU-Tree102 to address the issue of scarce diversity in current benchmarks. Experiments on three benchmark datasets demonstrate that our approach achieves state-of-the-art performance. Crucially, as a lightweight plug-and-play module, EKDC-Net improves backbone accuracy by 6.42% and precision by 11.46% using only 0.08M additional learnable parameters. The dataset, code, and pre-trained models are available at https://github.com/WHU-USI3DV/TreeCLS.

翻译：精确的细粒度树种分类对于森林资源清查和生物多样性监测至关重要。现有方法主要侧重于设计复杂架构以拟合局部数据分布。然而，这些方法往往忽视了有限数据中固有的长尾分布和高类间相似性，从而难以区分少样本或易混淆类别。在人类世界的知识传播过程中，个体会主动寻求专家协助以超越局部思维的局限。受此启发，我们引入外部"领域专家"，并提出一种专家知识引导的分类决策校准网络（EKDC-Net）来应对这些挑战。我们的框架解决两个核心问题：专家知识提取与利用。具体而言，我们首先开发了局部先验引导知识提取模块（LPKEM）。通过利用类激活图（CAM）分析，LPKEM引导领域专家仅关注对分类至关重要的判别性特征。随后，为有效整合该知识，我们设计了不确定性引导决策校准模块（UDCM）。该模块通过综合考虑整体类别不确定性和实例级预测不确定性，动态校正局部模型的决策。此外，针对当前基准数据集多样性不足的问题，我们提出了一个涵盖102个树种的大规模分类数据集CU-Tree102。在三个基准数据集上的实验表明，我们的方法实现了最先进的性能。至关重要的是，作为轻量级即插即用模块，EKDC-Net仅增加0.08M可学习参数，即可将主干网络准确率提升6.42%，精确率提升11.46%。数据集、代码与预训练模型已发布于https://github.com/WHU-USI3DV/TreeCLS。