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Digital Twins serve as virtual counterparts, replicating the characteristics and functionalities of tangible objects, processes, or systems within the digital space, leveraging their capability to simulate and forecast real-world behavior. They have found valuable applications in smart farming, facilitating a comprehensive virtual replica of a farm that encompasses vital aspects such as crop cultivation, soil composition, and prevailing weather conditions. By amalgamating data from diverse sources, including soil, plants condition, environmental sensor networks, meteorological predictions, and high-resolution UAV and Satellite imagery, farmers gain access to dynamic and up-to-date visualization of their agricultural domains empowering them to make well-informed and timely choices concerning critical aspects like efficient irrigation plans, optimal fertilization methods, and effective pest management strategies, enhancing overall farm productivity and sustainability. This research paper aims to present a comprehensive overview of the contemporary state of research on digital twins in smart farming, including crop modelling, precision agriculture, and associated technologies, while exploring their potential applications and their impact on agricultural practices, addressing the challenges and limitations such as data privacy concerns, the need for high-quality data for accurate simulations and predictions, and the complexity of integrating multiple data sources. Lastly, the paper explores the prospects of digital twins in agriculture, highlighting potential avenues for future research and advancement in this domain.

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This paper presents a new hybrid approach that is suitable for application to energy smart meter gateways, based on combining both blockchain and Secure Element (SE) technologies serving the roles of a secure distributed data storage system and an essential component for building a "root of trust" in IoT platforms simultaneously. Blockchain technology alone may not completely secure a transaction because it only guarantees data immutability, while in most cases, the data has to be also secured at the point of generation. The proposed combinational approach aims to build a robust root of trust by introducing the SE, which will provide IoT devices with trusted computed resources. The feasibility of the proposed method is validated by testing three different implementation scenarios, using different Secure Element systems (SES) combined with blockchain and LPWAN communication technologies to encrypt, transmit, and save data. This hybrid approach aids in overcoming the obstructions of using any one technology alone, and its use is demonstrated with a case study for an Energy Smart Metering gateway that enables the implementation of a local Peer to Peer energy trading scheme that is end-to-end secure and decentralized.

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During the last two years, the COVID-19 pandemic continues to wreak havoc in many areas of the world, as the infection spreads through person-to-person contact. Transmission and prognosis, once infected, are potentially influenced by many factors, including indoor air pollution. Particulate Matter (PM) is a complex mixture of solid and/or liquid particles suspended in the air that can vary in size, shape, and composition and recent scientific work correlate this index with a considerable risk of COVID-19 infections. Early Warning Systems (EWS) and the Internet of Things (IoT) have given rise to the development of Low Power Wide Area Networks (LPWAN) based on sensors, which measure PM levels and monitor In-door Air pollution Quality (IAQ) in real-time. This article proposes an open-source platform architecture and presents the development of a Long Range (LoRa) based sensor network for IAQ and PM measurement. A few air quality sensors were tested, a network platform was implemented after simulating setup topologies, emphasizing feasible low-cost open platform architecture.

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Integral imaging is one of the most promising techniques for delivering three-dimensional content. Most processing tasks usually require prior knowledge of the size and positions of the elemental images that comprise an integral image. In this paper we propose an automated method for calibrating the acquisition setup, by applying a preprocessing stage to an acquired integral image. The skew angle is extracted and the size and positions of the elemental images are accurately determined. For these purposes a method is developed to automatically identify an elemental image lattice that best matches the acquired integral image.