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...
Published in: | 2022 IEEE 18th International Colloquium on Signal Processing and Applications, CSPA 2022 - Proceeding |
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Language: | English |
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Institute of Electrical and Electronics Engineers Inc.
2022
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Online Access: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85132754076&doi=10.1109%2fCSPA55076.2022.9782022&partnerID=40&md5=4a43cee55303dbf2a3a450599bbb9b14 |
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Roslan N.A.M.; Mat Diah N.; Ibrahim Z.; Hanum H.M.; Ismail M. |
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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 |
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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 |
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container_issue |
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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. |
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Institute of Electrical and Electronics Engineers Inc. |
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English |
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Conference paper |
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scopus |
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Scopus |
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1828987869116497920 |