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      Precision Agriculture: A Remote Sensing Monitoring System Architecture †

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          Abstract

          Smart Farming is a development that emphasizes on the use of modern technologies in the cyber-physical field management cycle. Technologies such as the Internet of Things (IoT) and Cloud Computing have accelerated the digital transformation of the conventional agricultural practices promising increased production rate and product quality. The adoption of smart farming though is hampered because of the lack of models providing guidance to practitioners regarding the necessary components that constitute IoT-based monitoring systems. To guide the process of designing and implementing Smart farming monitoring systems, in this paper we propose a generic reference architecture model, taking also into consideration a very important non-functional requirement, the energy consumption restriction. Moreover, we present and discuss the technologies that incorporate the seven layers of the architecture model that are the Sensor Layer, the Link Layer, the Encapsulation Layer, the Middleware Layer, the Configuration Layer, the Management Layer and the Application Layer. Furthermore, the proposed Reference Architecture model is exemplified in a real-world application for surveying Saffron agriculture in Kozani, Greece.

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          Most cited references46

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          Big Data in Smart Farming – A review

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            ‘Structure-from-Motion’ photogrammetry: A low-cost, effective tool for geoscience applications

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              Internet of Things (IoT) for Smart Precision Agriculture and Farming in Rural Areas

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                Author and article information

                Contributors
                (View ORCID Profile)
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                Journal
                INFOGG
                Information
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                MDPI AG
                2078-2489
                November 2019
                November 09 2019
                : 10
                : 11
                : 348
                Article
                10.3390/info10110348
                88557f0d-7a3e-4afe-b2fa-ee6dbadb35a1
                © 2019

                https://creativecommons.org/licenses/by/4.0/

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