Smart farming has garnered significant attention due to substantial advancements in robotics and IoT technologies. However, these advancements necessitate robust data management and processing guidelines to fully harness the potential of data and optimize farm production. Unfortunately, such clear guidelines are lacking in the smart farming sector, forcing practitioners and researchers to implement custom architectures for specific scenarios. This survey paper aims to examine the advancements in data management and processing within the Internet of Robotic Things (IoRT) context. After showing that the existing surveys on IoRT and smart farming barely cover these issues, we will review and classify the related literature within the framework of a reference architecture. We will conclude by listing the main open issues to be addressed in order to achieve the full potential of data-driven practices in the smart farming field.
Bazza, H., Bimonte, S., Rizzi, S., Badir, H. (2025). Data Management and Processing for IoT & Robotics in Smart Farming: A Survey. JOURNAL OF COMPUTER LANGUAGES, 85, 1-16 [10.1016/j.cola.2025.101355].
Data Management and Processing for IoT & Robotics in Smart Farming: A Survey
Stefano Rizzi;
2025
Abstract
Smart farming has garnered significant attention due to substantial advancements in robotics and IoT technologies. However, these advancements necessitate robust data management and processing guidelines to fully harness the potential of data and optimize farm production. Unfortunately, such clear guidelines are lacking in the smart farming sector, forcing practitioners and researchers to implement custom architectures for specific scenarios. This survey paper aims to examine the advancements in data management and processing within the Internet of Robotic Things (IoRT) context. After showing that the existing surveys on IoRT and smart farming barely cover these issues, we will review and classify the related literature within the framework of a reference architecture. We will conclude by listing the main open issues to be addressed in order to achieve the full potential of data-driven practices in the smart farming field.| File | Dimensione | Formato | |
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embargo fino al 15/08/2026
Tipo:
Postprint / Author's Accepted Manuscript (AAM) - versione accettata per la pubblicazione dopo la peer-review
Licenza:
Licenza per Accesso Aperto. Creative Commons Attribuzione - Non commerciale - Non opere derivate (CCBYNCND)
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836.65 kB
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Adobe PDF
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