RoMu4o: A Robotic Manipulation Unit For Orchard Operations Automating Proximal Hyperspectral Leaf Sensing

• URL: http://arxiv.org/abs/2501.10621v1
• Date: Sat, 18 Jan 2025 01:04:02 GMT
• Title: RoMu4o: A Robotic Manipulation Unit For Orchard Operations Automating Proximal Hyperspectral Leaf Sensing
• Authors: Mehrad Mortazavi, David J. Cappelleri, Reza Ehsani,
• Abstract summary: 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. This work introduces RoMu4o, a robotic manipulation unit for orchard operations offering an automated solution for proximal hyperspectral leaf sensing.
• Score: 2.1038216828914145
• License:
• Abstract: Driven by the need to address labor shortages and meet the demands of a rapidly growing population, robotic automation has become a critical component in precision agriculture. 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. This work introduces RoMu4o, a robotic manipulation unit for orchard operations offering an automated solution for proximal hyperspectral leaf sensing. This ground robot is equipped with a 6DOF robotic arm and vision system for real-time deep learning-based image processing and motion planning. We developed robust perception and manipulation pipelines that enable the robot to successfully grasp target leaves and perform spectroscopy. These frameworks operate synergistically to identify and extract the 3D structure of leaves from an observed batch of foliage, propose 6D poses, and generate collision-free constraint-aware paths for precise leaf manipulation. The end-effector of the arm features a compact design that integrates an independent lighting source with a hyperspectral sensor, enabling high-fidelity data acquisition while streamlining the calibration process for accurate measurements. Our ground robot is engineered to operate in unstructured orchard environments. However, the performance of the system is evaluated in both indoor and outdoor plant models. The system demonstrated reliable performance for 1-LPB hyperspectral sampling, achieving 95% success rate in lab trials and 79% in field trials. Field experiments revealed an overall success rate of 70% for autonomous leaf grasping and hyperspectral measurement in a pistachio orchard. The open-source repository is available at: https://github.com/mehradmrt/UCM-AgBot-ROS2

Related papers

• Automated Phytosensing: Ozone Exposure Classification Based on Plant Electrical Signals [10.274619512179882]
  We propose to use a decentralized network of living plants as air-quality sensors by measuring their electrophysiology to infer the environmental state. We show that our approach successfully classifies plant ozone exposure with accuracies of up to 94.6% on unseen data. Our results help implement significant advancements for phytosensing devices contributing to the development of cost-effective, high-density urban air monitoring systems.
  arXiv  Detail & Related papers  (2024-12-17T20:29:00Z)
• Polaris: Open-ended Interactive Robotic Manipulation via Syn2Real Visual Grounding and Large Language Models [53.22792173053473]
  We introduce an interactive robotic manipulation framework called Polaris. Polaris integrates perception and interaction by utilizing GPT-4 alongside grounded vision models. We propose a novel Synthetic-to-Real (Syn2Real) pose estimation pipeline.
  arXiv  Detail & Related papers  (2024-08-15T06:40:38Z)
• Plant Doctor: A hybrid machine learning and image segmentation software to quantify plant damage in video footage [0.0]
  This study introduces an AI-based system for the automatic diagnosis of urban street plants using video footage obtained with accessible camera devices. The system aims to monitor plant health on a day-to-day basis, aiding in the control of disease spreading in urban areas. The results demonstrate the robustness and accuracy of the system in diagnosing leaf damage, with potential applications in large scale urban flora illness monitoring.
  arXiv  Detail & Related papers  (2024-07-03T07:11:18Z)
• RoboScript: Code Generation for Free-Form Manipulation Tasks across Real and Simulation [77.41969287400977]
  This paper presents textbfRobotScript, a platform for a deployable robot manipulation pipeline powered by code generation. We also present a benchmark for a code generation benchmark for robot manipulation tasks in free-form natural language. We demonstrate the adaptability of our code generation framework across multiple robot embodiments, including the Franka and UR5 robot arms.
  arXiv  Detail & Related papers  (2024-02-22T15:12:00Z)
• Robotic Navigation Autonomy for Subretinal Injection via Intelligent Real-Time Virtual iOCT Volume Slicing [88.99939660183881]
  We propose a framework for autonomous robotic navigation for subretinal injection. Our method consists of an instrument pose estimation method, an online registration between the robotic and the i OCT system, and trajectory planning tailored for navigation to an injection target. Our experiments on ex-vivo porcine eyes demonstrate the precision and repeatability of the method.
  arXiv  Detail & Related papers  (2023-01-17T21:41:21Z)
• Autonomous Agriculture Robot for Smart Farming [0.0]
  AAR is a lightweight, solar-electric powered robot that uses intelligent perception for conducting detection and classification of plants and their characteristics. The robot can deliver fertilizer spraying, insecticide, herbicide, and other fluids to the targets such as crops, weeds, and other pests.
  arXiv  Detail & Related papers  (2022-08-02T19:38:48Z)
• Visual-tactile sensing for Real-time liquid Volume Estimation in Grasping [58.50342759993186]
  We propose a visuo-tactile model for realtime estimation of the liquid inside a deformable container. We fuse two sensory modalities, i.e., the raw visual inputs from the RGB camera and the tactile cues from our specific tactile sensor. The robotic system is well controlled and adjusted based on the estimation model in real time.
  arXiv  Detail & Related papers  (2022-02-23T13:38:31Z)
• Geometry-Aware Fruit Grasping Estimation for Robotic Harvesting in Orchards [6.963582954232132]
  geometry-aware network, A3N, is proposed to perform end-to-end instance segmentation and grasping estimation. We implement a global-to-local scanning strategy, which enables robots to accurately recognise and retrieve fruits in field environments. Overall, the robotic system achieves success rate of harvesting ranging from 70% - 85% in field harvesting experiments.
  arXiv  Detail & Related papers  (2021-12-08T16:17:26Z)
• Automated Pest Detection with DNN on the Edge for Precision Agriculture [0.0]
  This paper presents an embedded system enhanced with machine learning (ML) functionalities, ensuring continuous detection of pest infestation inside fruit orchards. Three different ML algorithms have been trained and deployed, highlighting the capabilities of the platform. Results show how it is possible to automate the task of pest infestation for unlimited time without the farmer's intervention.
  arXiv  Detail & Related papers  (2021-08-01T10:17:48Z)
• Online Body Schema Adaptation through Cost-Sensitive Active Learning [63.84207660737483]
  The work was implemented in a simulation environment, using the 7DoF arm of the iCub robot simulator. A cost-sensitive active learning approach is used to select optimal joint configurations. The results show cost-sensitive active learning has similar accuracy to the standard active learning approach, while reducing in about half the executed movement.
  arXiv  Detail & Related papers  (2021-01-26T16:01:02Z)
• Estimating Crop Primary Productivity with Sentinel-2 and Landsat 8 using Machine Learning Methods Trained with Radiative Transfer Simulations [58.17039841385472]
  We take advantage of all parallel developments in mechanistic modeling and satellite data availability for advanced monitoring of crop productivity. Our model successfully estimates gross primary productivity across a variety of C3 crop types and environmental conditions even though it does not use any local information from the corresponding sites. This highlights its potential to map crop productivity from new satellite sensors at a global scale with the help of current Earth observation cloud computing platforms.
  arXiv  Detail & Related papers  (2020-12-07T16:23:13Z)

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.