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Development of a Uav-Based Multi-Dimensional Mapping Framework for Precise Frost Management in Apple Orchards.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Development of a Uav-Based Multi-Dimensional Mapping Framework for Precise Frost Management in Apple Orchards./
Author:	Yuan, Wenan.
Description:	1 online resource (110 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 84-02, Section: B.
Contained By:	Dissertations Abstracts International84-02B.
Subject:	Trees. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29276522click for full text (PQDT)
ISBN:	9798841575559

Development of a Uav-Based Multi-Dimensional Mapping Framework for Precise Frost Management in Apple Orchards.
Yuan, Wenan.

Development of a Uav-Based Multi-Dimensional Mapping Framework for Precise Frost Management in Apple Orchards. - 1 online resource (110 pages)

Source: Dissertations Abstracts International, Volume: 84-02, Section: B.

Thesis (Ph.D.)--The Pennsylvania State University, 2022.

Includes bibliographical references

As one of the main causes of weather-related damages in agriculture, frost leads to significant economic losses for farmers worldwide. Yet, traditional frost protection and temperature assessment methods in orchards remain rudimentary. Unmanned aerial vehicles (UAVs) and airborne sensing instruments emerged in recent years as promising tools for assisting efficient and convenient crop monitoring and management in precision agriculture, which creates opportunities in revolutionizing orchard frost protection approaches. With an overarching goal of building an autonomous cyber-physical system (CPS) consisting of UAV-based sensing and unmanned ground vehicle (UGV)-based heating for precise frost management, in this dissertation, a multi-dimensional mapping framework was proposed to process UAV-based thermal imagery, RGB imagery, and light detection and ranging (LiDAR) point cloud data to extract growth stage, canopy temperature, and tree structural information of an apple orchard. A thermal image stitching algorithm was developed to create high-resolution orchard temperature maps. A convolutional neural network (CNN)-based classifier was developed for detecting apple flower buds in RGB images, whose robustness against artificial image distortions and training dataset attributes were also investigated in depth. A UAV-LiDAR system was developed for identifying orchard regions that were unsafe for UGV travelling. The final output of the mapping framework, georeferenced orchard navigation maps, indicated both orchard heating requirements and orchard open space regions, which can potentially serve as a guide for UGV path planning and heat treatment application during frost events in future studies.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2023

Mode of access: World Wide Web

ISBN: 9798841575559Subjects--Topical Terms:

516384
Trees.
Index Terms--Genre/Form:

542853
Electronic books.

Development of a Uav-Based Multi-Dimensional Mapping Framework for Precise Frost Management in Apple Orchards.
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245 1 0 $a Development of a Uav-Based Multi-Dimensional Mapping Framework for Precise Frost Management in Apple Orchards.
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300 $a 1 online resource (110 pages)
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500 $a Source: Dissertations Abstracts International, Volume: 84-02, Section: B.
500 $a Advisor: Choi, Daeun.
502 $a Thesis (Ph.D.)--The Pennsylvania State University, 2022.
504 $a Includes bibliographical references
520 $a As one of the main causes of weather-related damages in agriculture, frost leads to significant economic losses for farmers worldwide. Yet, traditional frost protection and temperature assessment methods in orchards remain rudimentary. Unmanned aerial vehicles (UAVs) and airborne sensing instruments emerged in recent years as promising tools for assisting efficient and convenient crop monitoring and management in precision agriculture, which creates opportunities in revolutionizing orchard frost protection approaches. With an overarching goal of building an autonomous cyber-physical system (CPS) consisting of UAV-based sensing and unmanned ground vehicle (UGV)-based heating for precise frost management, in this dissertation, a multi-dimensional mapping framework was proposed to process UAV-based thermal imagery, RGB imagery, and light detection and ranging (LiDAR) point cloud data to extract growth stage, canopy temperature, and tree structural information of an apple orchard. A thermal image stitching algorithm was developed to create high-resolution orchard temperature maps. A convolutional neural network (CNN)-based classifier was developed for detecting apple flower buds in RGB images, whose robustness against artificial image distortions and training dataset attributes were also investigated in depth. A UAV-LiDAR system was developed for identifying orchard regions that were unsafe for UGV travelling. The final output of the mapping framework, georeferenced orchard navigation maps, indicated both orchard heating requirements and orchard open space regions, which can potentially serve as a guide for UGV path planning and heat treatment application during frost events in future studies.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2023
538 $a Mode of access: World Wide Web
650 4 $a Trees. $3 516384
650 4 $a Unmanned aerial vehicles. $3 3560267
650 4 $a Cold. $3 560283
650 4 $a Heat. $3 573595
650 4 $a Sensors. $3 3549539
650 4 $a Aerospace engineering. $3 1002622
650 4 $a Engineering. $3 586835
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650 4 $a Transportation. $3 555912
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710 2 $a The Pennsylvania State University. $3 699896
773 0 $t Dissertations Abstracts International $g 84-02B.
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29276522 $z click for full text (PQDT)