Comparison of SVM, Naive Bayes, and ELM Models in Plant Growth Classification

This study investigates the application of machine learning models to predict plant growth milestones based on environmental and treatment data. The dataset comprises categorical variables such as soil type, water frequency, and fertilizer type, alongside numerical variables including sunlight ho...

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Main Authors:	M., Muflih, Silvia, Ratna, Haldi, Budiman, Usman, Syapotro, Muhammad, Hamdani
Format:	Article
Language:	English English
Published:	INTI International University 2024
Subjects:	QA75 Electronic computers. Computer science QA76 Computer software QK Botany T Technology (General)
Online Access:	http://eprints.intimal.edu.my/2055/ http://eprints.intimal.edu.my/2055/1/jods2024_56.pdf http://eprints.intimal.edu.my/2055/2/596

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author	M., Muflih Silvia, Ratna Haldi, Budiman Usman, Syapotro Muhammad, Hamdani
author_facet	M., Muflih Silvia, Ratna Haldi, Budiman Usman, Syapotro Muhammad, Hamdani
author_sort	M., Muflih
building	INTI Institutional Repository
collection	Online Access
description	This study investigates the application of machine learning models to predict plant growth milestones based on environmental and treatment data. The dataset comprises categorical variables such as soil type, water frequency, and fertilizer type, alongside numerical variables including sunlight hours, temperature, and humidity. Preprocessing involved one-hot encoding for categorical variables and standard scaling for numerical features. The models employed were Support Vector Machine (SVM), Naive Bayes, and Extreme Learning Machine (ELM). The baseline SVM model achieved an accuracy of 58.97%, and hyperparameter tuning using GridSearchCV did not improve this performance, maintaining the accuracy at 58.97%. The Naive Bayes model achieved an accuracy of 51.28%, while the ELM model had an accuracy of 43.85%. Among the models, the SVM demonstrated the highest accuracy, though further improvement is required for practical implementation. The findings underscore the importance of selecting appropriate machine learning models and optimizing their parameters to enhance prediction accuracy in agricultural applications. Despite the SVM's superior performance in this context, continued refinement is essential to address the challenges posed by predicting plant growth milestones accurately.
first_indexed	2025-11-14T11:58:37Z
format	Article
id	intimal-2055
institution	INTI International University
institution_category	Local University
language	English English
last_indexed	2025-11-14T11:58:37Z
publishDate	2024
publisher	INTI International University
recordtype	eprints
repository_type	Digital Repository
spelling	intimal-20552024-11-26T06:58:49Z http://eprints.intimal.edu.my/2055/ Comparison of SVM, Naive Bayes, and ELM Models in Plant Growth Classification M., Muflih Silvia, Ratna Haldi, Budiman Usman, Syapotro Muhammad, Hamdani QA75 Electronic computers. Computer science QA76 Computer software QK Botany T Technology (General) This study investigates the application of machine learning models to predict plant growth milestones based on environmental and treatment data. The dataset comprises categorical variables such as soil type, water frequency, and fertilizer type, alongside numerical variables including sunlight hours, temperature, and humidity. Preprocessing involved one-hot encoding for categorical variables and standard scaling for numerical features. The models employed were Support Vector Machine (SVM), Naive Bayes, and Extreme Learning Machine (ELM). The baseline SVM model achieved an accuracy of 58.97%, and hyperparameter tuning using GridSearchCV did not improve this performance, maintaining the accuracy at 58.97%. The Naive Bayes model achieved an accuracy of 51.28%, while the ELM model had an accuracy of 43.85%. Among the models, the SVM demonstrated the highest accuracy, though further improvement is required for practical implementation. The findings underscore the importance of selecting appropriate machine learning models and optimizing their parameters to enhance prediction accuracy in agricultural applications. Despite the SVM's superior performance in this context, continued refinement is essential to address the challenges posed by predicting plant growth milestones accurately. INTI International University 2024-11 Article PeerReviewed text en cc_by_4 http://eprints.intimal.edu.my/2055/1/jods2024_56.pdf text en cc_by_4 http://eprints.intimal.edu.my/2055/2/596 M., Muflih and Silvia, Ratna and Haldi, Budiman and Usman, Syapotro and Muhammad, Hamdani (2024) Comparison of SVM, Naive Bayes, and ELM Models in Plant Growth Classification. Journal of Data Science, 2024 (56). pp. 1-6. ISSN 2805-5160 http://ipublishing.intimal.edu.my/jods.html
spellingShingle	QA75 Electronic computers. Computer science QA76 Computer software QK Botany T Technology (General) M., Muflih Silvia, Ratna Haldi, Budiman Usman, Syapotro Muhammad, Hamdani Comparison of SVM, Naive Bayes, and ELM Models in Plant Growth Classification
title	Comparison of SVM, Naive Bayes, and ELM Models in Plant Growth Classification
title_full	Comparison of SVM, Naive Bayes, and ELM Models in Plant Growth Classification
title_fullStr	Comparison of SVM, Naive Bayes, and ELM Models in Plant Growth Classification
title_full_unstemmed	Comparison of SVM, Naive Bayes, and ELM Models in Plant Growth Classification
title_short	Comparison of SVM, Naive Bayes, and ELM Models in Plant Growth Classification
title_sort	comparison of svm, naive bayes, and elm models in plant growth classification
topic	QA75 Electronic computers. Computer science QA76 Computer software QK Botany T Technology (General)
url	http://eprints.intimal.edu.my/2055/ http://eprints.intimal.edu.my/2055/ http://eprints.intimal.edu.my/2055/1/jods2024_56.pdf http://eprints.intimal.edu.my/2055/2/596

Comparison of SVM, Naive Bayes, and ELM Models in Plant Growth Classification

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