東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

The application of unmanned aerial v...

Elarab, Manal.

Linked to FindBook

Google Book

Amazon

博客來

The application of unmanned aerial vehicle to precision agriculture: Chlorophyll, nitrogen, and evapotranspiration estimation.

Record Type:	Electronic resources : Monograph/item
Title/Author:	The application of unmanned aerial vehicle to precision agriculture: Chlorophyll, nitrogen, and evapotranspiration estimation./
Author:	Elarab, Manal.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2015,
Description:	148 p.
Notes:	Source: Dissertation Abstracts International, Volume: 77-11(E), Section: B.
Contained By:	Dissertation Abstracts International77-11B(E).
Subject:	Agronomy. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10117360
ISBN:	9781339787626

The application of unmanned aerial vehicle to precision agriculture: Chlorophyll, nitrogen, and evapotranspiration estimation.
Elarab, Manal.

The application of unmanned aerial vehicle to precision agriculture: Chlorophyll, nitrogen, and evapotranspiration estimation. - Ann Arbor : ProQuest Dissertations & Theses, 2015 - 148 p.

Source: Dissertation Abstracts International, Volume: 77-11(E), Section: B.

Thesis (Ph.D.)--Utah State University, 2015.

Precision agriculture (PA) is an integration of a set of technologies aiming to improve productivity and profitability while sustaining the quality of the surrounding environment. It is a process that vastly relies on high-resolution information to enable greater precision in the management of inputs to production. This dissertation explored the usage of multispectral high resolution aerial imagery acquired by an unmanned aerial systems (UAS) platform to serve precision agriculture application. The UAS acquired imagery in the visual, near infrared and thermal infrared spectra with a resolution of less than a meter (15--60 cm). This research focused on developing two models to estimate cm-scale chlorophyll content and leaf nitrogen. To achieve the estimations a well-established machine learning algorithm (relevance vector machine) was used. The two models were trained on a dataset of in situ collected leaf chlorophyll and leaf nitrogen measurements, and the machine learning algorithm intelligently selected the most appropriate bands and indices for building regressions with the highest prediction accuracy. In addition, this research explored the usage of the high resolution imagery to estimate crop evapotranspiration (ET) at 15 cm resolution. A comparison was also made between the high resolution ET and Landsat derived ET over two different crop cover (field crops and vineyards) to assess the advantages of UAS based high resolution ET. This research aimed to bridge the information embedded in the high resolution imagery with ground crop parameters to provide site specific information to assist farmers adopting precision agriculture. The framework of this dissertation consisted of three components that provide tools to support precision agriculture operational decisions. In general, the results for each of the methods developed were satisfactory, relevant, and encouraging.

ISBN: 9781339787626Subjects--Topical Terms:

2122783
Agronomy.

The application of unmanned aerial vehicle to precision agriculture: Chlorophyll, nitrogen, and evapotranspiration estimation.
LDR:02878nmm a2200301 4500 001 2120919
005 20170724102928.5
008 180830s2015 ||||||||||||||||| ||eng d
020 $a 9781339787626
035 $a (MiAaPQ)AAI10117360
035 $a AAI10117360
040 $a MiAaPQ $c MiAaPQ
100 1 $a Elarab, Manal. $3 3282887
245 1 4 $a The application of unmanned aerial vehicle to precision agriculture: Chlorophyll, nitrogen, and evapotranspiration estimation.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2015
300 $a 148 p.
500 $a Source: Dissertation Abstracts International, Volume: 77-11(E), Section: B.
500 $a Adviser: Mac McKee.
502 $a Thesis (Ph.D.)--Utah State University, 2015.
520 $a Precision agriculture (PA) is an integration of a set of technologies aiming to improve productivity and profitability while sustaining the quality of the surrounding environment. It is a process that vastly relies on high-resolution information to enable greater precision in the management of inputs to production. This dissertation explored the usage of multispectral high resolution aerial imagery acquired by an unmanned aerial systems (UAS) platform to serve precision agriculture application. The UAS acquired imagery in the visual, near infrared and thermal infrared spectra with a resolution of less than a meter (15--60 cm). This research focused on developing two models to estimate cm-scale chlorophyll content and leaf nitrogen. To achieve the estimations a well-established machine learning algorithm (relevance vector machine) was used. The two models were trained on a dataset of in situ collected leaf chlorophyll and leaf nitrogen measurements, and the machine learning algorithm intelligently selected the most appropriate bands and indices for building regressions with the highest prediction accuracy. In addition, this research explored the usage of the high resolution imagery to estimate crop evapotranspiration (ET) at 15 cm resolution. A comparison was also made between the high resolution ET and Landsat derived ET over two different crop cover (field crops and vineyards) to assess the advantages of UAS based high resolution ET. This research aimed to bridge the information embedded in the high resolution imagery with ground crop parameters to provide site specific information to assist farmers adopting precision agriculture. The framework of this dissertation consisted of three components that provide tools to support precision agriculture operational decisions. In general, the results for each of the methods developed were satisfactory, relevant, and encouraging.
590 $a School code: 0241.
650 4 $a Agronomy. $3 2122783
650 4 $a Agricultural engineering. $3 3168406
650 4 $a Remote sensing. $3 535394
690 $a 0285
690 $a 0539
690 $a 0799
710 2 $a Utah State University. $b Civil and Environmental. $3 1023746
773 0 $t Dissertation Abstracts International $g 77-11B(E).
790 $a 0241
791 $a Ph.D.
792 $a 2015
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10117360