Staff View: Implementation of machine learning for predicting maize crop yields using multiple linear regression and backward elimination / Stephen Gbenga Fashoto … [et al.]

Implementation of machine learning for predicting maize crop yields using multiple linear regression and backward elimination / Stephen Gbenga Fashoto … [et al.]

Predicting maize crop yields especially in maize production is paramount in order to alleviate poverty and contribute towards food security. Many regions experience food shortage especially in Africa because of uncertain climatic changes, poor irrigation facilities, reduction in soil fertility and t...

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Main Authors:	Fashoto, Stephen Gbenga, Mbunge, Elliot, Ogunleye, Gabriel, den Burg, Johan Van
Format:	Article
Language:	English
Published:	Universiti Teknologi MARA 2021
Subjects:	Multivariate analysis. Cluster analysis. Longitudinal method Analytic mechanics
Online Access:	http://ir.uitm.edu.my/id/eprint/47823/1/47823.pdf http://ir.uitm.edu.my/id/eprint/47823/ https://mjoc.uitm.edu.my
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id	my.uitm.ir.47823
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spelling	my.uitm.ir.478232021-06-21T08:32:23Z http://ir.uitm.edu.my/id/eprint/47823/ Implementation of machine learning for predicting maize crop yields using multiple linear regression and backward elimination / Stephen Gbenga Fashoto … [et al.] Fashoto, Stephen Gbenga Mbunge, Elliot Ogunleye, Gabriel den Burg, Johan Van Multivariate analysis. Cluster analysis. Longitudinal method Analytic mechanics Predicting maize crop yields especially in maize production is paramount in order to alleviate poverty and contribute towards food security. Many regions experience food shortage especially in Africa because of uncertain climatic changes, poor irrigation facilities, reduction in soil fertility and traditional farming techniques. Therefore, predicting maize crop yields helps policymakers to make timely import and export decisions to strengthen national food security. However, none of the published work has been done to predict maize crop yields using machine learning in Eswatini, Africa. This paper aimed at applying machine learning (ML) to predict maize yields for a single season in Eswatini. A ML model was trained and tested using open-source data and local data. This is done by using three different data splits with the opensource predictor data consisting of 48 data points each with 7 attributes and open-source response data consisting of 48 data points each with a single attribute, adjusted R² values were 0.784 (at 70:30), 0.849 (at 80:20), and 0.878 (at 90:10) before being normalized, 1.00 across the board after normalization, and 0.846 (at 70:30), 0.886 (at 80:20), and 0.885 (at 90:10) after backward elimination. At the second attempt, it is done by using the combined predictor data of 68 data points with 7 attributes each and combined response data of 68 data points with a single attribute each, with the same data splits and methods adjusted R² values were 0.966 (at 70:30), 0.972 (at 80:20), and 0.978 (at 90:10) before being normalized, 1.00 across the board after normalization, and 0.967 (at 70:30), 0.973 (at 80:20), and 0.978 (at 90:10) after backward elimination. Universiti Teknologi MARA 2021-01 Article PeerReviewed text en http://ir.uitm.edu.my/id/eprint/47823/1/47823.pdf ID47823 Fashoto, Stephen Gbenga and Mbunge, Elliot and Ogunleye, Gabriel and den Burg, Johan Van (2021) Implementation of machine learning for predicting maize crop yields using multiple linear regression and backward elimination / Stephen Gbenga Fashoto … [et al.]. Malaysian Journal of Computing (MJoC), 6 (1). pp. 679-697. ISSN (eISSN): 2600-8238 https://mjoc.uitm.edu.my
institution	Universiti Teknologi Mara
building	Tun Abdul Razak Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Teknologi Mara
content_source	UiTM Institutional Repository
url_provider	http://ir.uitm.edu.my/
language	English
topic	Multivariate analysis. Cluster analysis. Longitudinal method Analytic mechanics
spellingShingle	Multivariate analysis. Cluster analysis. Longitudinal method Analytic mechanics Fashoto, Stephen Gbenga Mbunge, Elliot Ogunleye, Gabriel den Burg, Johan Van Implementation of machine learning for predicting maize crop yields using multiple linear regression and backward elimination / Stephen Gbenga Fashoto … [et al.]
description	Predicting maize crop yields especially in maize production is paramount in order to alleviate poverty and contribute towards food security. Many regions experience food shortage especially in Africa because of uncertain climatic changes, poor irrigation facilities, reduction in soil fertility and traditional farming techniques. Therefore, predicting maize crop yields helps policymakers to make timely import and export decisions to strengthen national food security. However, none of the published work has been done to predict maize crop yields using machine learning in Eswatini, Africa. This paper aimed at applying machine learning (ML) to predict maize yields for a single season in Eswatini. A ML model was trained and tested using open-source data and local data. This is done by using three different data splits with the opensource predictor data consisting of 48 data points each with 7 attributes and open-source response data consisting of 48 data points each with a single attribute, adjusted R² values were 0.784 (at 70:30), 0.849 (at 80:20), and 0.878 (at 90:10) before being normalized, 1.00 across the board after normalization, and 0.846 (at 70:30), 0.886 (at 80:20), and 0.885 (at 90:10) after backward elimination. At the second attempt, it is done by using the combined predictor data of 68 data points with 7 attributes each and combined response data of 68 data points with a single attribute each, with the same data splits and methods adjusted R² values were 0.966 (at 70:30), 0.972 (at 80:20), and 0.978 (at 90:10) before being normalized, 1.00 across the board after normalization, and 0.967 (at 70:30), 0.973 (at 80:20), and 0.978 (at 90:10) after backward elimination.
format	Article
author	Fashoto, Stephen Gbenga Mbunge, Elliot Ogunleye, Gabriel den Burg, Johan Van
author_facet	Fashoto, Stephen Gbenga Mbunge, Elliot Ogunleye, Gabriel den Burg, Johan Van
author_sort	Fashoto, Stephen Gbenga
title	Implementation of machine learning for predicting maize crop yields using multiple linear regression and backward elimination / Stephen Gbenga Fashoto … [et al.]
title_short	Implementation of machine learning for predicting maize crop yields using multiple linear regression and backward elimination / Stephen Gbenga Fashoto … [et al.]
title_full	Implementation of machine learning for predicting maize crop yields using multiple linear regression and backward elimination / Stephen Gbenga Fashoto … [et al.]
title_fullStr	Implementation of machine learning for predicting maize crop yields using multiple linear regression and backward elimination / Stephen Gbenga Fashoto … [et al.]
title_full_unstemmed	Implementation of machine learning for predicting maize crop yields using multiple linear regression and backward elimination / Stephen Gbenga Fashoto … [et al.]
title_sort	implementation of machine learning for predicting maize crop yields using multiple linear regression and backward elimination / stephen gbenga fashoto … [et al.]
publisher	Universiti Teknologi MARA
publishDate	2021
url	http://ir.uitm.edu.my/id/eprint/47823/1/47823.pdf http://ir.uitm.edu.my/id/eprint/47823/ https://mjoc.uitm.edu.my
_version_	1703963485781622784
score	13.211869

Implementation of machine learning for predicting maize crop yields using multiple linear regression and backward elimination / Stephen Gbenga Fashoto … [et al.]

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