Data engineering for sustainable agriculture: developments, challenges, and case studies of a novel IoRT architecture

• Published in: Journal of big data Vol. 12; no. 1; pp. 195 - 29
• Main Authors: Bimonte, Sandro, Bellocchi, Gianni, Pinet, François, Chalhoub, Gérard, Sakr, Mahmoud, Skrzypczyński, Piotr
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 02.08.2025; Springer Nature B.V; SpringerOpen
• Subjects: Agricultural management; Artificial intelligence; Big Data; Case studies; Communication; Communications Engineering; Computational Science and Engineering; Computer Science; Data management; Data Mining and Knowledge Discovery; Data processing; Database Management; Datasets; Decision making; Edge computing; Emergent architectures and technologies for big data management and analysis; Engineering; Farmers; Farming; Information Storage and Retrieval; Internet of Things; Mathematical Applications in Computer Science; Metadata; Methodology; Networks; Quality management; Real time; Robotics; Robots; Sensors; Sustainability; Sustainable agriculture; Sustainable agriculture; Data management; Internet of Things; Robotics
• ISSN: 2196-1115; 2196-1115
• DOI: 10.1186/s40537-025-01253-z

In recent years, the integration of Internet of Things (IoT) and Robotics has advanced agro-ecological practices. Despite numerous solutions for specific farming applications, a comprehensive reference architecture that addresses the diverse data management and operational requirements of agro-ecology remains absent. This paper introduces a concept of novel architecture for an Internet of Robotic Things (IoRT) system designed to manage the voluminous, real-time, and heterogeneous data in agricultural applications. By leveraging existing frameworks and technologies, this architecture aims to provide robust data management, ensure data quality, and support sustainable farming practices. The proposed architecture is demonstrated through case studies, which show its applicability and efficiency in real-world scenarios. This work is aimed at providing a guideline for researchers and engineers in developing and implementing smart farming solutions with Big Data, IoT and Robotics.