Purpose. Global population growth leads to increasing food demand. Hydroponics farming is a resourceefficient solution to eliminate soil dependency. The research focuses on integrating IoT sensors, Machine Learning (ML) and Big Data Analytics into a fully automated, self-regulating agricultural ecosystem to maximize crop yield, resource efficiency and sustainability. Method. After an extensive literature review to analyse the ideal environmental conditions and the latest smart agriculture technologies, the framework was developed integrating I4.0. Finally, the benefits have been evaluated. Findings. IoT for continuous real-time monitoring ensures precise control over nutrient delivery, water usage and environmental conditions. ML analyses data for predictive adjustments, plant growth enhancement and resource optimization. The system reaches three times the crop yield per unit area of conventional methods. Automated dashboards and AI-powered decisionmaking improve operational efficiency and facilitate remote monitoring and intervention. Conclusions. The study confirms that data-driven hydroponic systems enhance efficiency, sustainability and scalability in modern agriculture.
Conceptual Framework for Smart 4.0 Hydroponic Farming: A Data-Driven Approach to Sustainable Agriculture
Briatore, Federico
;Melesse, Tsega Y.;Arena, Simone;Braggio, Mattia
2025-01-01
Abstract
Purpose. Global population growth leads to increasing food demand. Hydroponics farming is a resourceefficient solution to eliminate soil dependency. The research focuses on integrating IoT sensors, Machine Learning (ML) and Big Data Analytics into a fully automated, self-regulating agricultural ecosystem to maximize crop yield, resource efficiency and sustainability. Method. After an extensive literature review to analyse the ideal environmental conditions and the latest smart agriculture technologies, the framework was developed integrating I4.0. Finally, the benefits have been evaluated. Findings. IoT for continuous real-time monitoring ensures precise control over nutrient delivery, water usage and environmental conditions. ML analyses data for predictive adjustments, plant growth enhancement and resource optimization. The system reaches three times the crop yield per unit area of conventional methods. Automated dashboards and AI-powered decisionmaking improve operational efficiency and facilitate remote monitoring and intervention. Conclusions. The study confirms that data-driven hydroponic systems enhance efficiency, sustainability and scalability in modern agriculture.
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