结合三维骨架提取与语言分析的多模态植物根表型分析
Multimodal Plant Root Phenotyping with Integration of 3D Skeleton Extraction and Language Analysis
AI总结：
该研究提出多模态机器人AI框架，结合W-LBC无监督三维骨架提取与证据优先语言建模，微调GPT实现12种植物根表型的可解释分析，建立了定量与语义结合的根表型统一分析范式。
AI中文摘要：
植物根表型分析是理解地下结构、优化作物管理和提升农业可持续性的基础。本文提出一种多模态机器人AI框架，将三维骨架提取与语言引导推理相结合，用于可解释且数据高效的根分析。我们开发了一种基于加权拉普拉斯收缩（W-LBC）的无监督骨架提取网络，可从机器人三维感知平台采集的密集点云中生成高保真结构表征。从重构的骨架图中计算根数量、长度、分支角度和密度等定量形态描述符，以捕捉几何与拓扑特征。基于这些特征，我们引入证据优先语言建模框架，利用自动生成的指令-响应对微调GPT，使其成为交互式分析聊天机器人。每个训练样本在自然语言推理前提供可测量证据，使模型能将解释锚定在定量形态学上。通过监督微调，GPT将数值结构与语义意义关联，生成符合生物学规律的生长模式和适应性性状解释。实验表明，该结构引导框架在12种具有不同根结构的植物物种上实现了鲁棒、可解释的推理。通过整合无监督三维几何感知与大规模语言理解，我们的方法弥合了定量分析与语义解释之间的鸿沟，为可解释机器人植物根表型分析建立了统一范式。
英文摘要：
Plant root phenotyping is fundamental to understanding below-ground structures, optimizing crop management, and improving agricultural sustainability. This paper presents a multimodal robotic AI framework that integrates 3D skeleton extraction with language-guided reasoning for interpretable and data-efficient root analysis. We develop an unsupervised skeleton extraction network based on Weighted Laplacian Contraction (W-LBC) to generate high-fidelity structural representations from dense point clouds captured by robotic 3D sensing platforms. Quantitative morphological descriptors, including root count, length, branching angle, and density, are computed from the reconstructed skeleton graph to capture geometric and topological characteristics. Building on these features, we introduce an Evidence-First language modeling framework that fine-tunes GPT as an interactive analytical chatbot using automatically generated instruction--response pairs. Each training sample provides measurable evidence before natural-language reasoning, enabling the model to ground interpretation in quantitative morphology. Through supervised fine-tuning, GPT associates numerical structure with semantic meaning, producing biologically consistent explanations of growth patterns and adaptive traits. Experiments show that the structure-guided framework achieves robust, interpretable reasoning across 12 plant species with diverse root architectures. By integrating unsupervised 3D geometric perception with large-scale language understanding, our approach bridges quantitative analysis and semantic interpretation, establishing a unified paradigm for explainable robotic plant root phenotyping.
