RESEARCH ARTICLE


Empowering Crop Selection with Ensemble Learning and K-means Clustering: A Modern Agricultural Perspective



K.P. Swain1, Soumya Ranjan Nayak2, Vinayakumar Ravi7, *, Sarita Mishra3, Tahani Jaser Alahmadi8, *, Prabhishek Singh4, Manoj Diwakar5, 6
1 Department of ETC, Trident Academy of Technology, Bhubaneswar, Odisha, India
2 School of Computer Engineering, KIIT Deemed to be University, Bhubaneswar 751024, Odisha, India
3 Department of ECE, GITA Autonomous College, Bhubaneswar, Odisha, India
4 School of Computer Science Engineering and Technology, Bennett University, Greater Noida, India
5 Department of CSE, Graphic Era Deemed to be University, Dehradun, Uttarakhand, India
6 Graphic Era Hill University, Dehradun, Uttarakhand, India
7 Center for Artificial Intelligence, Prince Mohammad Bin Fahd University, Khobar, Saudi Arabia
8 Department of Information Systems, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia

Article Information


Identifiers and Pagination:

Year: 2024
Volume: 18
E-location ID: e18743315291367
Publisher ID: e18743315291367
DOI: 10.2174/0118743315291367240207093403

Article History:

Received Date: 12/12/2023
Revision Received Date: 25/01/2024
Acceptance Date: 29/01/2024
Electronic publication date: 14/02/2024
Collection year: 2024

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Creative Commons License
© 2024 The Author(s). Published by Bentham Open.

open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: https://creativecommons.org/licenses/by/4.0/legalcode. This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

* Address correspondence to these authors at the Center for Artificial Intelligence, Prince Mohammad Bin Fahd University, Khobar, Saudi Arabia and Department of Information Systems, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia; E-mails: vinayakumarr77@gmail.com, tjalahmadi@pnu.edu.sa


Abstract

Introduction

Agriculture is an intricate blend of scientific principles and practical techniques that facilitate the growth of crops and the cultivation of livestock. It involves the careful cultivation of the land to produce essential food, fibers, and various other agricultural products.

Methods

Effective agricultural planning fosters self-sufficiency in food production, offers a source of income for farmers, and contributes to government revenue. This research focuses on utilizing ensemble learning techniques and K-means clustering to predict optimal crop types for specific environmental conditions and categorize crops according to their environmental requirements. This approach aims to refine crop selection strategies significantly.

Results

The study, employing a comprehensive dataset, applies these advanced methods, yielding accurate predictions and deeper insights into the interaction between crops and their growing environments.

Conclusion

These findings suggest a potential revolution in agricultural decision-making, highlighting the benefits of these methodologies in enhancing crop yield, reducing environmental impact, and promoting sustainable agricultural practices. The outcomes underscore the impact of data-driven approaches in modern agriculture, offering a promising direction for future agricultural development.

Keywords: Descriptive analysis, Crop prediction, K-Means clustering, Ensemble learning, Agriculture, Agriculture, Crop selection.