國立虎尾科技大學 |

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

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	AI-Enabled Big Data Pipeline for Plant Phenotyping and Application in Cotton Bloom Detection and Counting./
作者:	Issac, Amanda.
面頁冊數:	1 online resource (71 pages)
附註:	Source: Masters Abstracts International, Volume: 84-12.
Contained By:	Masters Abstracts International84-12.
標題:	Computer science. -
電子資源:	click for full text (PQDT)
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. - 1 online resource (71 pages)

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

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

Includes bibliographical references

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.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798379693480Subjects--Topical Terms:

573171
Computer science.
Subjects--Index Terms:

Big dataIndex Terms--Genre/Form:

554714
Electronic books.

AI-Enabled Big Data Pipeline for Plant Phenotyping and Application in Cotton Bloom Detection and Counting.
LDR:02541ntm a22004097 4500 001 1142005
005 20240414211920.5
006 m o d
007 cr mn ---uuuuu
008 250605s2023 xx obm 000 0 eng d
020 $a 9798379693480
035 $a (MiAaPQ)AAI30315367
035 $a AAI30315367
040 $a MiAaPQ $b eng $c MiAaPQ $d NTU
100 1 $a Issac, Amanda. $3 1466143
245 1 0 $a AI-Enabled Big Data Pipeline for Plant Phenotyping and Application in Cotton Bloom Detection and Counting.
264 0 $c 2023
300 $a 1 online resource (71 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Source: Masters Abstracts International, Volume: 84-12.
500 $a Advisor: Velni, Javad Mohammadpour.
502 $a Thesis (M.S.)--University of Georgia, 2023.
504 $a Includes bibliographical references
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.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2024
538 $a Mode of access: World Wide Web
650 4 $a Computer science. $3 573171
650 4 $a Information science. $3 561178
653 $a Big data
653 $a Computer vision
653 $a Lambda architecture
653 $a Microsoft azure
653 $a Plant phenotyping
653 $a Principal Component Analysis
655 7 $a Electronic books. $2 local $3 554714
690 $a 0800
690 $a 0984
690 $a 0723
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a University of Georgia. $b Artificial Intelligence - MS. $3 1466144
773 0 $t Masters Abstracts International $g 84-12.
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30315367 $z click for full text (PQDT)