AGRARIAN: A Hybrid AI-Driven Architecture for Smart Agriculture

Michael C. Batistatos, Tomaso de Cola, Michail Alexandros Kourtis (), Vassiliki Apostolopoulou, George K. Xilouris and Nikos C. Sagias
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Michael C. Batistatos: Department of Informatics and Telecommunications, University of Peloponnese, 22100 Tripolis, Greece
Tomaso de Cola: Institute of Communications and Navigation, Deutsches Zentrum für Luft- und Raumfahrt (DLR) Oberpfaffenhofen, 82234 Wessling, Germany
Michail Alexandros Kourtis: Institute of Informatics and Telecommunications, National Centre for Scientific Research “DEMOKRITOS” (NCSRD), 15310 Athens, Greece
Vassiliki Apostolopoulou: Practin, Kastritsa, 45500 Ioannina, Greece
George K. Xilouris: Institute of Informatics and Telecommunications, National Centre for Scientific Research “DEMOKRITOS” (NCSRD), 15310 Athens, Greece
Nikos C. Sagias: Department of Informatics and Telecommunications, University of Peloponnese, 22100 Tripolis, Greece

Agriculture, 2025, vol. 15, issue 8, 1-15

Abstract: Modern agriculture is increasingly challenged by the need for scalable, sustainable, and connectivity-resilient digital solutions. While existing smart farming platforms offer valuable insights, they often rely heavily on centralized cloud infrastructure, which can be impractical in rural or remote settings. To address this gap, this paper presents AGRARIAN, a hybrid AI-driven architecture that combines IoT sensor networks, UAV-based monitoring, satellite connectivity, and edge-cloud computing to deliver real-time, adaptive agricultural intelligence. AGRARIAN supports a modular and interoperable architecture structured across four layers—Sensor, Network, Data Processing, and Application—enabling flexible deployment in diverse use cases such as precision irrigation, livestock monitoring, and pest forecasting. A key innovation lies in its localized edge processing and federated AI models, which reduce reliance on continuous cloud access while maintaining analytical performance. Pilot scenarios demonstrate the system’s ability to provide timely, context-aware decision support, enhancing both operational efficiency and digital inclusion for farmers. AGRARIAN offers a robust and scalable pathway for advancing autonomous, sustainable, and connected farming systems.

Keywords: smart agriculture; AI-driven decision support systems; precision farming (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2025
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