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International Journal of Advanced Engineering Application

ISSN: 3048-6807

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Integrated IoT and Machine Learning Frameworks for Precision Agriculture in Semi-Arid Regions

Author(s):Robert K. Stevens, Maria G. Lopez, David J. Wu

Affiliation: Department of Agricultural Engineering, Green Valley State College, Springfield, USA

Page No: 37-41

Volume issue & Publishing Year: Volume 3, Issue 2, 2026-02-21

Journal: International Journal of Advanced Engineering Application (IJAEA)

ISSN NO: 3048-6807

DOI: https://doi.org/10.5281/zenodo.18813673

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Abstract:
The escalating global water crisis necessitates a transition from traditional irrigation methods to data-driven precision agriculture. This study proposes an integrated framework combining Internet of Things (IoT) sensor networks with Machine Learning (ML) algorithms to optimize water usage in semi-arid agricultural zones. We deployed a network of soil moisture, temperature, and humidity sensors across a 10-acre test plot at Green Valley State College. Data was processed using a Random Forest Regressor to predict irrigation needs 24 hours in advance. Our results indicate a 22% reduction in water consumption and a 12% improvement in crop yield compared to traditional timer-based systems. This research demonstrates that affordable, localized IoT solutions can provide a scalable pathway for small-scale farmers to adopt sustainable practices

Keywords: Precision Agriculture; Internet of Things (IoT); Machine Learning; Smart Irrigation; Water Conservation; Random Forest Regressor

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