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GeoAI for Crop Seed Composition and ...

Dilmuart, Kamila.

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GeoAI for Crop Seed Composition and Yield Estimation from Standing Crops: A Multiscale Remote Sensing Analysis.

Record Type:	Electronic resources : Monograph/item
Title/Author:	GeoAI for Crop Seed Composition and Yield Estimation from Standing Crops: A Multiscale Remote Sensing Analysis./
Author:	Dilmuart, Kamila.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2023,
Description:	140 p.
Notes:	Source: Dissertations Abstracts International, Volume: 84-11, Section: B.
Contained By:	Dissertations Abstracts International84-11B.
Subject:	Agriculture. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30521545
ISBN:	9798379571979

GeoAI for Crop Seed Composition and Yield Estimation from Standing Crops: A Multiscale Remote Sensing Analysis.
Dilmuart, Kamila.

GeoAI for Crop Seed Composition and Yield Estimation from Standing Crops: A Multiscale Remote Sensing Analysis. - Ann Arbor : ProQuest Dissertations & Theses, 2023 - 140 p.

Source: Dissertations Abstracts International, Volume: 84-11, Section: B.

Thesis (Ph.D.)--Saint Louis University, 2023.

This item is not available from ProQuest Dissertations & Theses.

Anticipating a global population of nine billion by 2050, food security necessitates a 70% increase in agricultural production. Compounding this challenge are uncertainties such as climate change, limited natural resources, and population growth. Developing sustainable methods to enhance crop yield quality and quantity while adhering to financial and resource constraints is vital. This dissertation investigates the application of Geospatial Artificial Intelligence (GeoAI) in precision agriculture. The research aims to create adaptable methods for predicting standing crops' yield and seed composition using GeoAI and multiscale remote sensing data from UAV and satellite sources. The objectives are: (1) examining UAV-based multisensor Hyperspectral and LiDAR data fusion within the AutoML Machine Learning framework for precise crop yield prediction, (2) devising transferable remote sensing methods for forecasting soybean seed composition utilizing canopy spectral and structural information, and (3) assessing the multiscale applicability of the established remote sensing method through satellite imagery-based soybean seed composition prediction. By integrating GeoAI and multisensory data, this research introduces a robust, non-destructive, and scalable remote sensing-based prediction methodology for estimating crop yield and seed composition. These innovative methods hold the potential to transform agricultural practices, leading to breakthroughs in sustainable farming and making a significant contribution to global food security.

ISBN: 9798379571979Subjects--Topical Terms:

518588
Agriculture.
Subjects--Index Terms:

Crop phenotyping

GeoAI for Crop Seed Composition and Yield Estimation from Standing Crops: A Multiscale Remote Sensing Analysis.
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500 $a Source: Dissertations Abstracts International, Volume: 84-11, Section: B.
500 $a Advisor: Sagan, Vasit.
502 $a Thesis (Ph.D.)--Saint Louis University, 2023.
506 $a This item is not available from ProQuest Dissertations & Theses.
506 $a This item must not be sold to any third party vendors.
520 $a Anticipating a global population of nine billion by 2050, food security necessitates a 70% increase in agricultural production. Compounding this challenge are uncertainties such as climate change, limited natural resources, and population growth. Developing sustainable methods to enhance crop yield quality and quantity while adhering to financial and resource constraints is vital. This dissertation investigates the application of Geospatial Artificial Intelligence (GeoAI) in precision agriculture. The research aims to create adaptable methods for predicting standing crops' yield and seed composition using GeoAI and multiscale remote sensing data from UAV and satellite sources. The objectives are: (1) examining UAV-based multisensor Hyperspectral and LiDAR data fusion within the AutoML Machine Learning framework for precise crop yield prediction, (2) devising transferable remote sensing methods for forecasting soybean seed composition utilizing canopy spectral and structural information, and (3) assessing the multiscale applicability of the established remote sensing method through satellite imagery-based soybean seed composition prediction. By integrating GeoAI and multisensory data, this research introduces a robust, non-destructive, and scalable remote sensing-based prediction methodology for estimating crop yield and seed composition. These innovative methods hold the potential to transform agricultural practices, leading to breakthroughs in sustainable farming and making a significant contribution to global food security.
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653 $a Crop phenotyping
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653 $a Multispectral satellite imagery
653 $a Precision agriculture
653 $a Soybean protein
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