植物ストレスの不均一性を特徴づける能動センシング
Active sensing to characterize the heterogeneity of plant stress
植物の葉の蛍光測定を自動で行うロボットプラットフォームを提案。3D再構築と幾何解析、動作計画を組み合わせ、接触・近接センシングを実現する。
著者: Ayman Laaroussi, Peter Hanappe, David Colliaux
分類: cs.RO, cs.AI
原文アブストラクト
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.