Rice yield prediction in Dakshina Kannada district using ensemble machine learning

Authors

  • A. SHAILESH RAO Nitte (Deemed to be University), Nitte Meenakshi Institute of Technology (NMIT), Department of Mechanical Engineering, Bengaluru, India
  • ANJANA KRISHNAN CSIR-National Aerospace Laboratories, Bengaluru, India

DOI:

https://doi.org/10.54386/jam.v27i4.3148

Keywords:

Random Forest (RF), Support Vector Machines (SVM), k-Nearest Neighbours (kNN), Rice yield prediction

References

Aljuaydi, F. and Wu, Y.H. (2022). Multivariate machine learning-based prediction models of freeway traffic flow under non-recurrent events. Alexandria Engg. J., 65: 151–162. https://doi.org/10.1016/j.aej.2022.10.015

Mohapatra, S., Sahoo, D., Sahoo, A.K., Sharp, B. and Wen, L. (2023). Heterogeneous climate effect on crop yield and associated risks to water security in India. Intern. J. Water Resource Develop., 40(3): 345–378. https://doi.org/10.1080/07900627.2023.2244086

Moriondo, M., Maselli, F. and Bindi, M. (2006). A simple model of regional wheat yield based on NDVI data. Europ. J. Agron., 26(3): 266–274. https://doi.org/10.1016/j.eja.2006.10.007

Pang, A., Chang, M.W.L. and Chen, Y. (2022). Evaluation of Random Forests (RF) for regional and local-scale wheat yield prediction in Southeast Australia. Sensors, 22(3): 717. https://doi.org/10.3390/s22030717

Paudel, D., Boogaard, H., De Wit, A., Janssen, S., Osinga, S., Pylianidis, C. and Athanasiadis, I.N. (2020). Machine learning for large-scale crop yield forecasting. Agric. Syst., 187: 103016. https://doi.org/10.1016/j.agsy.2020.103016

Sakthipriya, D. and Chandrakumar, T. (2024). Weather based paddy yield prediction using machine learning regression algorithms. J. Agrometeorol., 26(3): 344-348. https://doi.org/10.54386/jam.v26i3.2598

Saravanan, K.S. and Bhagavathiappan, V. (2022). A comprehensive approach on predicting the crop yield using hybrid machine learning algorithms. J. Agrometeorol., 24(2): 179-185. https://doi.org/10.54386/jam.v24i2.1561

Shawon, S.M., Niha, F.L. and Zubair, H. (2024). Crop yield prediction using machine learning: An extensive and systematic literature review. Smart Agric. Technol., 100718. https://doi.org/10.1016/j.atech.2024.100718

Sutha, K., Indumathi, N. and Shankari, S.U. (2023). Recommending and predicting crop yield using Smart Machine Learning Algorithm (SMLA). Current Agric. Res. J., 11(2): 686–694. https://doi.org/10.12944/carj.11.2.30

Wani, S.P., Anantha, K. and Garg, K.K. (2017). Soil properties, crop yield, and economics under integrated crop management practices in Karnataka, southern India. World Develop., 93: 43–61. https://doi.org/10.1016/j.worlddev.2016.12.012

Zou, J. and Okhrin, O. (2024). Data-driven determination of plant growth stages for improved weather index insurance design. Agric. Finance Rev., 84(4/5): 297–319. https://doi.org/10.1108/afr-01-2024-0015

Downloads

Published

01-12-2025

How to Cite

RAO, A. S., & KRISHNAN, A. (2025). Rice yield prediction in Dakshina Kannada district using ensemble machine learning. Journal of Agrometeorology, 27(4), 518–521. https://doi.org/10.54386/jam.v27i4.3148

Issue

Vol. 27 No. 4 (2025): December

Section

Short Communication

Categories

License

Copyright (c) 2025 A. SHAILESH RAO, ANJANA KRISHNAN

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

This is a human-readable summary of (and not a substitute for) the license. Disclaimer.

You are free to:

Share — copy and redistribute the material in any medium or format

Adapt — remix, transform, and build upon the material

The licensor cannot revoke these freedoms as long as you follow the license terms.

    Under the following terms:

    Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.

    NonCommercial — You may not use the material for commercial purposes.

    ShareAlike — If you remix, transform, or build upon the material, you must distribute your contributions under the same license as the original.

    No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.

    Notices:

    You do not have to comply with the license for elements of the material in the public domain or where your use is permitted by an applicable exception or limitation.

    No warranties are given. The license may not give you all of the permissions necessary for your intended use. For example, other rights such as publicity, privacy, or moral rights may limit how you use the material.