Active sensing to characterize the heterogeneity of plant stress

• URL: http://arxiv.org/abs/2608.27088v1
• Date: Thu, 27 Aug 2026 13:11:13 GMT
• Title: Active sensing to characterize the heterogeneity of plant stress
• Abstract summary: We present an autonomous robotic platform designed to perform targeted fluorescence measurements on plant leaves.<n>The system combines 3D plant reconstruction, geometric analysis, and motion planning to localize suitable measurement points.<n>By tightly coupling perception, geometric reasoning, and manipulation, the proposed system provides a robotics-driven approach to high-resolution plant phenotyping.
• Score: 0.8602553195689513
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• AdaDexGrasp: Adaptive Dexterous Grasping via 3D Visuo-Tactile Representation Fusion [60.79507611737292]
  Humans achieve stable and adaptive grasps by seamlessly integrating visual perception and tactile feedback.<n>Existing robotic grasping approaches rely on visual inputs and lack mechanisms for tactile-guided adaptation after contact.<n>We propose a unified visuo-tactile-fusion grasping framework that integrates grasp generation, feasibility prediction, and adaptive refinement.
  arXiv  Detail & Related papers  (2026-08-06T16:29:11Z)
• Learning to Infer Parameterized Representations of Plants from 3D Scans [2.6774002558989705]
  We train a neural network with virtual plants generated using an L-systems-based procedural model.<n>After training, the network allows to infer a parametric tree-like representation based on an input 3D point cloud.<n>We evaluate our approach on Chenopodium Album plants, using experiments on synthetic plants to show that our unified framework allows for different tasks.
  arXiv  Detail & Related papers  (2025-05-28T13:23:48Z)
• T-REX: Vision-Based System for Autonomous Leaf Detection and Grasp Estimation [5.059120569845977]
  T-Rex is a gantry-based robotic system developed for autonomous leaf localization, selection, and grasping in greenhouse environments.<n>The system integrates a 6-degree-of-freedom manipulator with a stereo vision pipeline to identify and interact with target leaves.
  arXiv  Detail & Related papers  (2025-05-03T02:17:45Z)
• A Survey on 3D Reconstruction Techniques in Plant Phenotyping: From Classical Methods to Neural Radiance Fields (NeRF), 3D Gaussian Splatting (3DGS), and Beyond [15.48852238904361]
  Plant phenotyping plays a pivotal role in understanding plant traits and their interactions with the environment.<n>3D reconstruction technologies have emerged as powerful tools for capturing detailed plant morphology and structure.<n>This paper provides a review of the 3D reconstruction techniques for plant phenotyping.
  arXiv  Detail & Related papers  (2025-04-30T02:04:23Z)
• A Low-Cost Photogrammetry System for 3D Plant Modeling and Phenotyping [0.2356141385409842]
  We present an open-source, low-cost photogrammetry system for 3D plant modeling and phenotyping.<n>The system uses a structure-from-motion approach to reconstruct 3D representations of the plants via point clouds.
  arXiv  Detail & Related papers  (2025-04-23T16:02:52Z)
• RoMu4o: A Robotic Manipulation Unit For Orchard Operations Automating Proximal Hyperspectral Leaf Sensing [2.1038216828914145]
  Leaf-level hyperspectral spectroscopy is shown to be a powerful tool for phenotyping, monitoring crop health, identifying essential nutrients within plants as well as detecting diseases and water stress.<n>This work introduces RoMu4o, a robotic manipulation unit for orchard operations offering an automated solution for proximal hyperspectral leaf sensing.
  arXiv  Detail & Related papers  (2025-01-18T01:04:02Z)
• Flatten Anything: Unsupervised Neural Surface Parameterization [76.4422287292541]
  We introduce the Flatten Anything Model (FAM), an unsupervised neural architecture to achieve global free-boundary surface parameterization. Compared with previous methods, our FAM directly operates on discrete surface points without utilizing connectivity information. Our FAM is fully-automated without the need for pre-cutting and can deal with highly-complex topologies.
  arXiv  Detail & Related papers  (2024-05-23T14:39:52Z)
• Semantics-Aware Next-best-view Planning for Efficient Search and Detection of Task-relevant Plant Parts [3.9074818653555554]
  Searching and detecting the task-relevant parts of plants is important to automate harvesting and de-leafing of tomato plants.<n>Current active-vision algorithms cannot differentiate between relevant and irrelevant plant parts.<n>This work proposes a semantics-aware active-vision strategy that uses semantic information to identify the relevant plant parts.
  arXiv  Detail & Related papers  (2023-06-16T12:22:19Z)
• Pose-Oriented Transformer with Uncertainty-Guided Refinement for 2D-to-3D Human Pose Estimation [51.00725889172323]
  We propose a Pose-Oriented Transformer (POT) with uncertainty guided refinement for 3D human pose estimation. We first develop novel pose-oriented self-attention mechanism and distance-related position embedding for POT to explicitly exploit the human skeleton topology. We present an Uncertainty-Guided Refinement Network (UGRN) to refine pose predictions from POT, especially for the difficult joints.
  arXiv  Detail & Related papers  (2023-02-15T00:22:02Z)
• Semantic Image Segmentation with Deep Learning for Vine Leaf Phenotyping [59.0626764544669]
  In this study, we use Deep Learning methods to semantically segment grapevine leaves images in order to develop an automated object detection system for leaf phenotyping. Our work contributes to plant lifecycle monitoring through which dynamic traits such as growth and development can be captured and quantified.
  arXiv  Detail & Related papers  (2022-10-24T14:37:09Z)
• 3D Human Body Reshaping with Anthropometric Modeling [59.51820187982793]
  Reshaping accurate and realistic 3D human bodies from anthropometric parameters poses a fundamental challenge for person identification, online shopping and virtual reality. Existing approaches for creating such 3D shapes often suffer from complex measurement by range cameras or high-end scanners. This paper proposes a novel feature-selection-based local mapping technique, which enables automatic anthropometric parameter modeling for each body facet.
  arXiv  Detail & Related papers  (2021-04-05T04:09:39Z)
• Nothing But Geometric Constraints: A Model-Free Method for Articulated Object Pose Estimation [89.82169646672872]
  We propose an unsupervised vision-based system to estimate the joint configurations of the robot arm from a sequence of RGB or RGB-D images without knowing the model a priori. We combine a classical geometric formulation with deep learning and extend the use of epipolar multi-rigid-body constraints to solve this task.
  arXiv  Detail & Related papers  (2020-11-30T20:46:48Z)
• Risk-Averse MPC via Visual-Inertial Input and Recurrent Networks for Online Collision Avoidance [95.86944752753564]
  We propose an online path planning architecture that extends the model predictive control (MPC) formulation to consider future location uncertainties. Our algorithm combines an object detection pipeline with a recurrent neural network (RNN) which infers the covariance of state estimates. The robustness of our methods is validated on complex quadruped robot dynamics and can be generally applied to most robotic platforms.
  arXiv  Detail & Related papers  (2020-07-28T07:34:30Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.