Proceedings of the International Conference on Intelligent Systems for a Sustainable Future (ISSF 2026)

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Comparative Analysis of Machine Learning Approaches for Crop Recommendation in Sustainable Agriculture in India

Authors
Shivani Yadav1, Hari Kumar Singh1, *, Akriti Garg1, Mohd Mustafa Khan2
1Department of ECE, F.E.T, M.J.P. Rohilkhand University, Bareilly, India
2Department of CS&IT, F.E.T, M.J.P. Rohilkhand University, Bareilly, India
*Corresponding author. Email: hks@mjpru.ac.in
Corresponding Author
Hari Kumar Singh
Available Online 16 June 2026.
DOI
10.2991/978-94-6239-693-7_57How to use a DOI?
Keywords
Crop recommendation; Machine Learning; Sustainable Agriculture; Classification; Random Forest; Data-Driven Farming
Abstract

This study analyzes machine learning methods for crop recommendation using an agricultural dataset that contains soil nutrients (N, P, and K), pH value, temperature, humidity, and rainfall. Several machine learning models were used and assessed, including Decision Tree, Random Forest, Gradient Boosting, Extra Tree, Ada Boost, and XG Boost. The accuracy for XG-Boost is 99.60%, AdaBoost is 99.55%, Extra Trees is 99.40%, Random Forest is 99.32%, Decision Tree is 98.64%, and Gradient Boosting is 98.18%, according to experimental results. The findings show how ML-based methodologies can assist farmers in making data-driven crop selections, boosting output and promoting sustainable farming practices.

Copyright
© 2026 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 International Conference on Intelligent Systems for a Sustainable Future (ISSF 2026)
Series
Atlantis Highlights in Intelligent Systems
Publication Date
16 June 2026
ISBN
978-94-6239-693-7
ISSN
2589-4919
DOI
10.2991/978-94-6239-693-7_57How to use a DOI?
Copyright
© 2026 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  - Shivani Yadav
AU  - Hari Kumar Singh
AU  - Akriti Garg
AU  - Mohd Mustafa Khan
PY  - 2026
DA  - 2026/06/16
TI  - Comparative Analysis of Machine Learning Approaches for Crop Recommendation in Sustainable Agriculture in India
BT  - Proceedings of the International Conference on Intelligent Systems for a Sustainable Future (ISSF 2026)
PB  - Atlantis Press
SP  - 578
EP  - 586
SN  - 2589-4919
UR  - https://doi.org/10.2991/978-94-6239-693-7_57
DO  - 10.2991/978-94-6239-693-7_57
ID  - Yadav2026
ER  -