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AI-Enabled Big Data Pipeline for Plant Phenotyping and Application in Cotton Bloom Detection and Counting.

Record Type:	Electronic resources : Monograph/item
Title/Author:	AI-Enabled Big Data Pipeline for Plant Phenotyping and Application in Cotton Bloom Detection and Counting.
Author:	Issac, Amanda.
Published:	Ann Arbor : ProQuest Dissertations & Theses, 2023
Description:	71 p.
Notes:	Source: Masters Abstracts International, Volume: 84-12.
Notes:	Advisor: Velni, Javad Mohammadpour.
Contained By:	Masters Abstracts International84-12.
Subject:	Computer science.
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30315367
ISBN:	9798379693480

AI-Enabled Big Data Pipeline for Plant Phenotyping and Application in Cotton Bloom Detection and Counting.
Issac, Amanda.

AI-Enabled Big Data Pipeline for Plant Phenotyping and Application in Cotton Bloom Detection and Counting. - Ann Arbor : ProQuest Dissertations & Theses, 2023 - 71 p.

Source: Masters Abstracts International, Volume: 84-12.

Thesis (M.S.)--University of Georgia, 2023.

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

With a rapidly growing global population, meeting increasing agricultural demands has become imperative. Smart farming, powered by machine learning, has the potential to address this issue but faces hurdles in managing big data with high velocity. In this study, we implement a comprehensive big data pipeline for cotton bloom detection that utilizes Azure cloud computing resources and employs YOLOv5 for real-time and batch processing. The model achieves a high mean Average Precision (mAP) score of 0.96 for cotton bloom classification using 2021 data. We also explore the use of Principal Component Analysis (PCA) as a compression method to optimize pipeline execution time and storage space. Rigorously tested for scalability on distinct 2022 data, our pipeline incorporates downsampling with masking as an effective pre-processing step to reduce computational overhead while preserving accuracy. This research under-scores the potential of cloud computing in driving efficient big data processing in precision agriculture, enabling accurate crop yield prediction through advanced plant phenotyping techniques.

ISBN: 9798379693480Subjects--Topical Terms:

199325
Computer science.
Subjects--Index Terms:

Big data

AI-Enabled Big Data Pipeline for Plant Phenotyping and Application in Cotton Bloom Detection and Counting.
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502 $a Thesis (M.S.)--University of Georgia, 2023.
506 $a This item must not be sold to any third party vendors.
520 $a With a rapidly growing global population, meeting increasing agricultural demands has become imperative. Smart farming, powered by machine learning, has the potential to address this issue but faces hurdles in managing big data with high velocity. In this study, we implement a comprehensive big data pipeline for cotton bloom detection that utilizes Azure cloud computing resources and employs YOLOv5 for real-time and batch processing. The model achieves a high mean Average Precision (mAP) score of 0.96 for cotton bloom classification using 2021 data. We also explore the use of Principal Component Analysis (PCA) as a compression method to optimize pipeline execution time and storage space. Rigorously tested for scalability on distinct 2022 data, our pipeline incorporates downsampling with masking as an effective pre-processing step to reduce computational overhead while preserving accuracy. This research under-scores the potential of cloud computing in driving efficient big data processing in precision agriculture, enabling accurate crop yield prediction through advanced plant phenotyping techniques.
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653 $a Computer vision
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653 $a Microsoft azure
653 $a Plant phenotyping
653 $a Principal Component Analysis
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