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Multimodal Remote Sensing Data Fusio...

Maimaitijiang, Maitiniyazi.

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Multimodal Remote Sensing Data Fusion and Machine Learning for Crop Monitoring and Food Security.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Multimodal Remote Sensing Data Fusion and Machine Learning for Crop Monitoring and Food Security./
Author:	Maimaitijiang, Maitiniyazi.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2020,
Description:	230 p.
Notes:	Source: Dissertations Abstracts International, Volume: 81-12, Section: B.
Contained By:	Dissertations Abstracts International81-12B.
Subject:	Remote sensing. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=27962999
ISBN:	9798607377106

Multimodal Remote Sensing Data Fusion and Machine Learning for Crop Monitoring and Food Security.
Maimaitijiang, Maitiniyazi.

Multimodal Remote Sensing Data Fusion and Machine Learning for Crop Monitoring and Food Security. - Ann Arbor : ProQuest Dissertations & Theses, 2020 - 230 p.

Source: Dissertations Abstracts International, Volume: 81-12, Section: B.

Thesis (Ph.D.)--Saint Louis University, 2020.

This item must not be sold to any third party vendors.

Crop monitoring and yield prediction over agricultural fields are critical to grain policy making and food security in the context of climate change and population growth. Monitoring crop growth status such as crop biochemical & biophysical traits, and early estimation of yield at field/farm scales and mapping within-field yield spatial variations play an important role in crop management in terms of fertilization, irrigation and pesticides application, as well as in increasing crop production and subsequent profit while reducing input resources and environmental pollution. Moreover, non-destructive crop monitoring and yield estimation with high-accuracy at low-cost are needed for high-throughput plant phenotyping. Traditional approaches of monitoring crop are destructive, labor-intensive, time-consuming and not operationally feasible for large-scale spatial and temporal measurements.Remote sensing data provide timely, non-destructive, instantaneously and economically accurate estimations of the earth's surface over large areas, and it has been recognized as a valuable tool for crop monitoring and yield prediction.The main objective of this research is to develop and implement new approaches of crop monitoring and yield prediction within the framework of multimodal data fusion and machine/deep learning, through leveraging the advantages of remote sensing data from different platforms/scales (i.e. Satellite and UAV) and sensors (RGB, Multispectral, Hyperspectral, Thermal and LiDAR).

ISBN: 9798607377106Subjects--Topical Terms:

535394
Remote sensing.
Subjects--Index Terms:

Crop monitoring

Multimodal Remote Sensing Data Fusion and Machine Learning for Crop Monitoring and Food Security.
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500 $a Source: Dissertations Abstracts International, Volume: 81-12, Section: B.
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502 $a Thesis (Ph.D.)--Saint Louis University, 2020.
506 $a This item must not be sold to any third party vendors.
520 $a Crop monitoring and yield prediction over agricultural fields are critical to grain policy making and food security in the context of climate change and population growth. Monitoring crop growth status such as crop biochemical & biophysical traits, and early estimation of yield at field/farm scales and mapping within-field yield spatial variations play an important role in crop management in terms of fertilization, irrigation and pesticides application, as well as in increasing crop production and subsequent profit while reducing input resources and environmental pollution. Moreover, non-destructive crop monitoring and yield estimation with high-accuracy at low-cost are needed for high-throughput plant phenotyping. Traditional approaches of monitoring crop are destructive, labor-intensive, time-consuming and not operationally feasible for large-scale spatial and temporal measurements.Remote sensing data provide timely, non-destructive, instantaneously and economically accurate estimations of the earth's surface over large areas, and it has been recognized as a valuable tool for crop monitoring and yield prediction.The main objective of this research is to develop and implement new approaches of crop monitoring and yield prediction within the framework of multimodal data fusion and machine/deep learning, through leveraging the advantages of remote sensing data from different platforms/scales (i.e. Satellite and UAV) and sensors (RGB, Multispectral, Hyperspectral, Thermal and LiDAR).
590 $a School code: 0193.
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653 $a Crop monitoring
653 $a Data fusion
653 $a Deep learning
653 $a Food security
653 $a Machine learning
653 $a Remote sensing
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