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

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

Download

[thumbnail of 47823.pdf] Text
47823.pdf

Download (915kB)
Official URL: https://mjoc.uitm.edu.my

Abstract

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.

Metadata

Item Type: Article
Creators:
Creators
Email
Fashoto, Stephen Gbenga
gbengafash2@gmail.com
Mbunge, Elliot
mbungeelliot@gmail.com
Ogunleye, Gabriel
Ope992000@yahoo.com
den Burg, Johan Van
johanvdburg93@gmail.com
Subjects: Q Science > QA Mathematics > Multivariate analysis. Cluster analysis. Longitudinal method
Q Science > QA Mathematics > Analytic mechanics
Divisions: Universiti Teknologi MARA, Shah Alam > Faculty of Computer and Mathematical Sciences
Journal or Publication Title: Malaysian Journal of Computing (MJoC)
UiTM Journal Collections: UiTM Journal > Malaysian Journal of Computing (MJoC)
ISSN: (eISSN): 2600-8238
Volume: 6
Number: 1
Page Range: pp. 679-697
Official URL: https://mjoc.uitm.edu.my
Item ID: 47823
Uncontrolled Keywords: Agricultural technology, Backward elimination, Environmental factors
URI: https://ir.uitm.edu.my/id/eprint/47823

ID Number

47823

Indexing


View in Google Scholar

Edit Item
Edit Item