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This paper introduces an autonomous robotic platform that integrates active physiological sensing, specifically chlorophyll fluorescence measurements, with advanced 3D plant reconstruction and geometric analysis. By employing motion planning to identify optimal measurement points on plant leaves, the system enables precise and repeatable physiological assessments that surpass traditional image-based phenotyping methods. The key result demonstrates that this approach significantly enhances the spatial resolution of plant stress characterization, paving the way for more effective agricultural inspection and manipulation.
Automated fluorescence measurements reveal plant stress with unprecedented precision, transforming how we assess agricultural health.
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.
