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Developing Decision Support Tools to...

Farag, Fared,

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Developing Decision Support Tools to Optimize Crop Production Using UAV-Based Multispectral Imagery /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Developing Decision Support Tools to Optimize Crop Production Using UAV-Based Multispectral Imagery // Fared Farag.
Author:	Farag, Fared,
Description:	1 electronic resource (90 pages)
Notes:	Source: Masters Abstracts International, Volume: 86-03.
Contained By:	Masters Abstracts International86-03.
Subject:	Computer science. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31145670
ISBN:	9798384077183

Developing Decision Support Tools to Optimize Crop Production Using UAV-Based Multispectral Imagery /
Farag, Fared,

Developing Decision Support Tools to Optimize Crop Production Using UAV-Based Multispectral Imagery /Fared Farag. - 1 electronic resource (90 pages)

Source: Masters Abstracts International, Volume: 86-03.

Multispectral imagery collected through unmanned aerial vehicles (UAVs) holds promise for optimizing crop production to meet the rising market demands. This study aimed to enhance the main two facets behind the UAV data pipeline: data collection and data analysis. Chapter II addresses data collection challenges by presenting a software tool designed to assist UAV operators in validating and identifying potential errors in the collected images. Through beta testing, the software effectively reduced operational costs and laid a solid foundation for subsequent data analysis. Chapter III delves into the data analysis facet to develop advanced modeling approaches for accurate and robust rice yield prediction through machine and deep learning techniques. The proposed models showed significant improvements compared to traditional approaches and hold promise in providing actionable insights for informed nitrogen applications. Overall, this study contributes to improving the usability of UAV data in precision agriculture, facilitating efficient crop production to sustainably meet growing population needs.

English

ISBN: 9798384077183Subjects--Topical Terms:

523869
Computer science.
Subjects--Index Terms:

Decision support tools

Developing Decision Support Tools to Optimize Crop Production Using UAV-Based Multispectral Imagery /
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100 1 $a Farag, Fared, $e author. $3 3829893
245 1 0 $a Developing Decision Support Tools to Optimize Crop Production Using UAV-Based Multispectral Imagery / $c Fared Farag.
264 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2024
300 $a 1 electronic resource (90 pages)
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500 $a Source: Masters Abstracts International, Volume: 86-03.
500 $a Advisors: Causey, Jason; Bellis, Emily Committee members: Green, Steven; Hashem, Ahmed; Nowlin, John.
502 $b M.S. $c Arkansas State University $d 2024.
520 $a Multispectral imagery collected through unmanned aerial vehicles (UAVs) holds promise for optimizing crop production to meet the rising market demands. This study aimed to enhance the main two facets behind the UAV data pipeline: data collection and data analysis. Chapter II addresses data collection challenges by presenting a software tool designed to assist UAV operators in validating and identifying potential errors in the collected images. Through beta testing, the software effectively reduced operational costs and laid a solid foundation for subsequent data analysis. Chapter III delves into the data analysis facet to develop advanced modeling approaches for accurate and robust rice yield prediction through machine and deep learning techniques. The proposed models showed significant improvements compared to traditional approaches and hold promise in providing actionable insights for informed nitrogen applications. Overall, this study contributes to improving the usability of UAV data in precision agriculture, facilitating efficient crop production to sustainably meet growing population needs.
546 $a English
590 $a School code: 1231
650 4 $a Computer science. $3 523869
650 4 $a Aerospace engineering. $3 1002622
650 4 $a Information technology. $3 532993
650 4 $a Agricultural engineering. $3 3168406
653 $a Decision support tools
653 $a Manifold learning
653 $a Multispectral imagery
653 $a Precision agriculture
653 $a Rice yield prediction
653 $a Spatiotemporal learning
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690 $a 0489
690 $a 0800
690 $a 0538
690 $a 0539
710 2 $a Arkansas State University. $b Computer Science. $e degree granting institution. $3 3829894
720 1 $a Causey, Jason $e degree supervisor.
720 1 $a Bellis, Emily $e degree supervisor.
773 0 $t Masters Abstracts International $g 86-03.
790 $a 1231
791 $a M.S.
792 $a 2024
856 4 0 $u https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31145670