Automatic Plant Recognition: A Survey of Relevant Algorithms

Plants are one of the most important elements since they provide oxygen, which is necessary for human survival. Plant recognition applications have been widely developed, and these applications can help botanists tackle various real-world problems. This paper reviews machine learning and deep learni...

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Published in:2022 IEEE 18th International Colloquium on Signal Processing and Applications, CSPA 2022 - Proceeding
Main Author: 2-s2.0-85132754076
Format: Conference paper
Language:English
Published: Institute of Electrical and Electronics Engineers Inc. 2022
Online Access:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85132754076&doi=10.1109%2fCSPA55076.2022.9782022&partnerID=40&md5=4a43cee55303dbf2a3a450599bbb9b14
id Roslan N.A.M.; Mat Diah N.; Ibrahim Z.; Hanum H.M.; Ismail M.
spelling Roslan N.A.M.; Mat Diah N.; Ibrahim Z.; Hanum H.M.; Ismail M.
2-s2.0-85132754076
Automatic Plant Recognition: A Survey of Relevant Algorithms
2022
2022 IEEE 18th International Colloquium on Signal Processing and Applications, CSPA 2022 - Proceeding


10.1109/CSPA55076.2022.9782022
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85132754076&doi=10.1109%2fCSPA55076.2022.9782022&partnerID=40&md5=4a43cee55303dbf2a3a450599bbb9b14
Plants are one of the most important elements since they provide oxygen, which is necessary for human survival. Plant recognition applications have been widely developed, and these applications can help botanists tackle various real-world problems. This paper reviews machine learning and deep learning algorithms discussed for plant recognition. Different algorithms used for plant identification and recognition research between the year 2007 until the year 2020 are reviewed. The main algorithms discussed are Convolutional Neural Network (CNN), Support Vector Machine (SVM), Artificial Neural Network (ANN), and K-Nearest Neighbours (KNN). This paper also compares the performance between selected algorithms and proposes the best technique from the research outcomes. © 2022 IEEE.
Institute of Electrical and Electronics Engineers Inc.

English
Conference paper

author 2-s2.0-85132754076
spellingShingle 2-s2.0-85132754076
Automatic Plant Recognition: A Survey of Relevant Algorithms
author_facet 2-s2.0-85132754076
author_sort 2-s2.0-85132754076
title Automatic Plant Recognition: A Survey of Relevant Algorithms
title_short Automatic Plant Recognition: A Survey of Relevant Algorithms
title_full Automatic Plant Recognition: A Survey of Relevant Algorithms
title_fullStr Automatic Plant Recognition: A Survey of Relevant Algorithms
title_full_unstemmed Automatic Plant Recognition: A Survey of Relevant Algorithms
title_sort Automatic Plant Recognition: A Survey of Relevant Algorithms
publishDate 2022
container_title 2022 IEEE 18th International Colloquium on Signal Processing and Applications, CSPA 2022 - Proceeding
container_volume
container_issue
doi_str_mv 10.1109/CSPA55076.2022.9782022
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85132754076&doi=10.1109%2fCSPA55076.2022.9782022&partnerID=40&md5=4a43cee55303dbf2a3a450599bbb9b14
description Plants are one of the most important elements since they provide oxygen, which is necessary for human survival. Plant recognition applications have been widely developed, and these applications can help botanists tackle various real-world problems. This paper reviews machine learning and deep learning algorithms discussed for plant recognition. Different algorithms used for plant identification and recognition research between the year 2007 until the year 2020 are reviewed. The main algorithms discussed are Convolutional Neural Network (CNN), Support Vector Machine (SVM), Artificial Neural Network (ANN), and K-Nearest Neighbours (KNN). This paper also compares the performance between selected algorithms and proposes the best technique from the research outcomes. © 2022 IEEE.
publisher Institute of Electrical and Electronics Engineers Inc.
issn
language English
format Conference paper
accesstype
record_format scopus
collection Scopus
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