Design and Performance Analysis of a Low Cost IoT Based Smart Irrigation System using Soil Moisture Sensors

Zhanar Sartabanova

Abstract

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The clustering of large and high-dimensional data is still a problem because of the problems of scalability, memory, and quality evaluation of the cluster. This paper also suggests a Hybrid Scalable Clustering Framework (HSCF) which is a combination of hierarchical and partition-based clustering algorithms to provide efficient and effective behavior at different scales of data. The framework uses the BIRCH clustering algorithm of memory efficient, incremental clustering coupled with fast and scalable centroid-based optimization with MiniBatchKMeans clustering algorithm. Elbow Method is used to have the optimal clustering configuration, to make the number of clusters manually chosen and thus minimize human involvement and enhance reproducibility. The quality of clustering is computationally assessed by the Silhouette Score which gives information about the cohesiveness within a cluster and the distance between clusters. Moreover, PCA is applied to dimensionality reduction and visualization, with the help of which it becomes possible to produce useful visualizations of the structure of clustering in reduced feature space. Experimental evidence shows that MiniBatchKMeans is more efficient in its computational speed with large datasets whereas BIRCH is more efficient in terms of memory usage and hierarchical representation. The proposed framework is the most appropriate in the application of the real-world large-scale data analysis. The combination of multi-scale assessment, automatic optimization and multi-scale clustering is the main innovation of this article with the ability to provide a complete solution to the contemporary clustering problems.

Keywords

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Clustering, BIRCH, MiniBatchKMeans, Dimensionality Reduction, Silhouette Analysis.

Reference

CRediT Author Statement

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The author reviewed the results and approved the final version of the manuscript.

Acknowledgements

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Authors thank Reviewers for taking the time and effort necessary to review the manuscript.

Funding

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No funding was received to assist with the preparation of this manuscript.

Ethics Declarations

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Conflict of interest

The authors have no conflicts of interest to declare that are relevant to the content of this article.

Availability of Data and Materials

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Data sharing is not applicable to this article as no new data were created or analysed in this study.

Author Information

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Contributions

All authors have equal contribution in the paper and all authors have read and agreed to the published version of the manuscript.

Corresponding Author

Zhanar Sartabanova

Rights and permissions

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This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International License (CC BY 4.0).
https://creativecommons.org/licenses/by/4.0/.

This license permits unrestricted use, sharing, distribution, reproduction, and adaptation in any medium or format, including for commercial purposes, provided that appropriate credit is given to the original author(s) and the source, a link to the license is provided, and any changes made are indicated.

Copyright

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© 2026 Zhanar Sartabanova. The author(s) retain copyright of the work. The author(s) grant the Journal of Smart and Sustainable Farming (JSSF) and its publisher, Ansis Publications, the right of first publication and the right to identify itself as the original publisher of the article.

Cite this Article

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Zhanar Sartabanova, “Design and Performance Analysis of a Low Cost IoT Based Smart Irrigation System using Soil Moisture Sensors”, Journal of Smart and Sustainable Farming, pp. 012-021, 2026, doi: 10.64026/JSSF/2026002.