Synergistic intelligence: a novel hybrid model for precision agriculture using k-means, naive Bayes, and knowledge graphs

Authors

  • Catherine N. Ogbizi-Ugbe
    Department of Computer Science, University of Calabar, Calabar, Nigeria
  • Osowomuabe Njama-Abang
    Department of Computer Science, University of Calabar, Calabar, Nigeria
  • Samuel Oladimeji
    Department of Computer Science, University of Calabar, Calabar, Nigeria
  • Idongetsit E. Eteng
    Department of Computer Science, University of Calabar, Calabar, Nigeria
  • Edim A. Emanuel
    Department of Computer Science, University of Calabar, Calabar, Nigeria

Keywords:

Hybrid knowledge discovery, Precision agriculture, K-means clustering, Knowledge graphs

Abstract

This study presents a novel hybrid knowledge discovery model integrating K-Means clustering, Naive Bayes classification, and Knowledge Graph technology to address interpretability and data heterogeneity challenges in precision agriculture. The proposed framework first applies K-Means to segment agro-ecological zones using multi-source data (soil, climate, satellite imagery), then employs Naive Bayes to classify crop productivity tiers, achieving 89% accuracy—surpassing standalone benchmarks (Naive Bayes: 86%, Random Forest: 87.5%). A Neo4j-based Knowledge Graph contextualizes these outputs, demonstrating 95% schema completeness and efficient querying (0.1559s latency), while enabling dynamic analysis of soil-climate-crop relationships. Pilot trials confirmed actionable impacts, including 22% reduced water use and 18% less fertilizer waste in targeted farms. By unifying unsupervised/supervised learning with semantic reasoning, this work advances scalable, interpretable decision support systems for sustainable agriculture, offering a replicable template for global food security initiatives.

Dimensions

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Published

2026-02-01

How to Cite

Synergistic intelligence: a novel hybrid model for precision agriculture using k-means, naive Bayes, and knowledge graphs. (2026). Journal of the Nigerian Society of Physical Sciences, 8(1), 2929. https://doi.org/10.46481/jnsps.2026.2929

Issue

Volume 8, Issue 1, February 2026

Section

Computer Science
Copyright & Licensing

Copyright (c) 2025 Catherine N. Ogbizi-Ugbe, Osowomuabe Njama-Abang, Samuel Oladimeji, Idongetsit E. Eteng, Edim A. Emanuel (Author)

Creative Commons License

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

How to Cite

Synergistic intelligence: a novel hybrid model for precision agriculture using k-means, naive Bayes, and knowledge graphs. (2026). Journal of the Nigerian Society of Physical Sciences, 8(1), 2929. https://doi.org/10.46481/jnsps.2026.2929

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