Vision-based monitoring of orchards with UAVs

Nikolaos Stefas; Haluk Bayram; Volkan Isler

doi:10.1016/j.compag.2019.05.023

Vision-based monitoring of orchards with UAVs

Nikolaos Stefas, Haluk Bayram, Volkan Isler

Computer Science and Engineering

Research output: Contribution to journal › Article › peer-review

5 Scopus citations

Abstract

We present an autonomous aerial system which can safely navigate inside the rows of an orchard for crop inspection, yield estimation and similar farm management tasks. Our operating environment has two practical constraints: GPS signal can be unreliable close to the ground and the trees can grow large branches that might block the robot's predetermined path. To address these challenges, we developed components for navigation, vision-based obstacle detection and avoidance and path planning for visual coverage. Results from a field demonstration in an apple orchard are presented showcasing the ability of our system to perform successful navigation and complete yield coverage of apple tree rows.

Original language	English (US)
Article number	104814
Journal	Computers and Electronics in Agriculture
Volume	163
DOIs	https://doi.org/10.1016/j.compag.2019.05.023
State	Published - Aug 2019

Bibliographical note

Funding Information:
This work is supported in part by NSF Awards 1111638, 1525045 and the MnDrive initiative.The authors thank Professors Emily Hoover, Cindy Tong and James Luby from the Department of Horticultural Science, University of Minnesota for access to their orchards and useful discussions.

Publisher Copyright:
© 2019 Elsevier B.V.

Keywords

Agricultural robotics
Dynamic obstacle avoidance
Unmanned aerial vehicles
Vision-based navigation

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title = "Vision-based monitoring of orchards with UAVs",

abstract = "We present an autonomous aerial system which can safely navigate inside the rows of an orchard for crop inspection, yield estimation and similar farm management tasks. Our operating environment has two practical constraints: GPS signal can be unreliable close to the ground and the trees can grow large branches that might block the robot's predetermined path. To address these challenges, we developed components for navigation, vision-based obstacle detection and avoidance and path planning for visual coverage. Results from a field demonstration in an apple orchard are presented showcasing the ability of our system to perform successful navigation and complete yield coverage of apple tree rows.",

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author = "Nikolaos Stefas and Haluk Bayram and Volkan Isler",

note = "Funding Information: This work is supported in part by NSF Awards 1111638, 1525045 and the MnDrive initiative.The authors thank Professors Emily Hoover, Cindy Tong and James Luby from the Department of Horticultural Science, University of Minnesota for access to their orchards and useful discussions. Publisher Copyright: {\textcopyright} 2019 Elsevier B.V.",

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language = "English (US)",

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AU - Bayram, Haluk

AU - Isler, Volkan

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N2 - We present an autonomous aerial system which can safely navigate inside the rows of an orchard for crop inspection, yield estimation and similar farm management tasks. Our operating environment has two practical constraints: GPS signal can be unreliable close to the ground and the trees can grow large branches that might block the robot's predetermined path. To address these challenges, we developed components for navigation, vision-based obstacle detection and avoidance and path planning for visual coverage. Results from a field demonstration in an apple orchard are presented showcasing the ability of our system to perform successful navigation and complete yield coverage of apple tree rows.

AB - We present an autonomous aerial system which can safely navigate inside the rows of an orchard for crop inspection, yield estimation and similar farm management tasks. Our operating environment has two practical constraints: GPS signal can be unreliable close to the ground and the trees can grow large branches that might block the robot's predetermined path. To address these challenges, we developed components for navigation, vision-based obstacle detection and avoidance and path planning for visual coverage. Results from a field demonstration in an apple orchard are presented showcasing the ability of our system to perform successful navigation and complete yield coverage of apple tree rows.

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Vision-based monitoring of orchards with UAVs

Abstract

Bibliographical note

Keywords

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