这个机器人平台能精确测量植物叶绿素荧光，为农业检测和植物操作提供了新思路。
研究人员开发了一种自主机器人平台，可对植物叶片进行靶向叶绿素荧光测量。该系统结合3D植物重建、几何分析和运动规划，能从多视图数据重建密集3D模型并提取候选叶片表面。平台实现了自动化、可重复且空间分辨的生理测量，超越了被动成像技术。
Active sensing to characterize the heterogeneity of plant stress
While most phenotyping platforms rely primarily on image-based measurements, advanced plant characterization requires the integration of active physiological sensing modali- ties such as chlorophyll fluorescence. We present an autonomous robotic platform designed to perform targeted fluorescence measurements on plant leaves. The system combines 3D plant reconstruction, geometric analysis, and motion planning to localize suitable measurement points and generate collision-free trajectories for a robotic manipulator. A dense 3D model of the plant is reconstructed from multi-view data and used to extract candidate leaf surfaces based on orientation, accessibility, and sensing constraints. These targets are then integrated into a task-level planning framework that guides the end-effector to precise contact or near-contact configurations required for point-based fluorescence acquisition. The platform enables automated, repeatable, and spatially resolved physiological measurements that go beyond passive imaging. By tightly coupling perception, geometric reasoning, and manipulation, the proposed system provides a robotics-driven approach to high-resolution plant phenotyping and opens new directions for autonomous agricultural inspection and plant-aware manipulation.