Proceedings of the 6th Biennial Conference of Organization for Women in Science for the Developing World Nigeria (OWSD 2023)

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Comparative Analysis of Classification Algorithms for Crop Yield Prediction

Authors
U. A. Okengwu1, *, L. N. Onyejegbu1, L. U. Oghenekaro1, M. O. Musa1, A. O. Ugbari1
1Department of Computer Science, University of Port Harcourt, Port Harcourt, Nigeria
*Corresponding author. Email: ugochi.okengwu@uniport.edu.ng
Corresponding Author
U. A. Okengwu
Available Online 18 December 2023.
DOI
10.2991/978-94-6463-306-1_22How to use a DOI?
Keywords
Machine Learning; Classification; Random Forest; Crop Yield forecast, Modeling
Abstract

A machine learning model is an essential tool for deciding which crops to produce and what to do during those crops’ growing seasons. The employment of various machine learning algorithms in research to forecast higher crop output has considerably benefited the agriculture sector. In this study, an appropriate crop recommendation solution is constructed utilizing a Kaggle dataset that incorporates several factors such as (N-Nitrogen, K-Potassium, P-Phosphorus, Humidity, pH value of the soil, rainfall and temperature). The major goal of this model is to estimate which crop would grow best on a given farm based on the parameters that were used to create the model. The evaluated models revealed random forest to have the highest prediction accuracy with a score of 99 percent, K-Nearest Neighbor was next with a score of 97 and logistic regression recorded 96 percent. Hence random forest produced the highest accuracy score of 0.99 in recommending appropriate crops to farmers especially in times of drought and low soil fertility.

Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the 6th Biennial Conference of Organization for Women in Science for the Developing World Nigeria (OWSD 2023)
Series
Advances in Biological Sciences Research
Publication Date
18 December 2023
ISBN
10.2991/978-94-6463-306-1_22
ISSN
2468-5747
DOI
10.2991/978-94-6463-306-1_22How to use a DOI?
Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - U. A. Okengwu
AU  - L. N. Onyejegbu
AU  - L. U. Oghenekaro
AU  - M. O. Musa
AU  - A. O. Ugbari
PY  - 2023
DA  - 2023/12/18
TI  - Comparative Analysis of Classification Algorithms for Crop Yield Prediction
BT  - Proceedings of the 6th Biennial Conference of Organization for Women in Science for the Developing World Nigeria (OWSD 2023)
PB  - Atlantis Press
SP  - 328
EP  - 342
SN  - 2468-5747
UR  - https://doi.org/10.2991/978-94-6463-306-1_22
DO  - 10.2991/978-94-6463-306-1_22
ID  - Okengwu2023
ER  -