RoMu4o: A Robotic Manipulation Unit For Orchard Operations Automating Proximal Hyperspectral Leaf Sensing
Mehrad Mortazavi, David J. Cappelleri, Reza Ehsani

TL;DR
RoMu4o is an autonomous robotic system designed for orchard leaf sensing that combines advanced perception, manipulation, and hyperspectral data collection to improve precision agriculture practices.
Contribution
This work introduces a novel robotic manipulation unit with integrated hyperspectral sensing and deep learning-based perception for orchard leaf analysis.
Findings
Achieved 95% success rate in lab hyperspectral sampling
Attained 79% success rate in outdoor field trials
Reached 70% success rate for autonomous leaf grasping in orchards
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…
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Taxonomy
TopicsSmart Agriculture and AI
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