A Systematic Review on Monitoring and Advanced Control Strategies in Smart Agriculture

Hassan, Syeda Iqra; Alam, Muhammad Mansoor; Illahi, Usman; Ghamdi, Mohammed A. Al; Almotiri, Sultan H.; Su’ud, Mazliham Mohd

doi:10.1109/access.2021.3057865

Cited by 98 publications

(40 citation statements)

References 144 publications

(86 reference statements)

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“…Access control algorithms based on cipher text is one of the solutions to preserve confidentiality in smart agriculture [124]. Moreover, blockchain-based access control mechanisms can be adopted in smart agriculture systems.…”

Section: ) Smart Agriculturementioning

confidence: 99%

Security in the Internet of Things Application Layer: Requirements, Threats, and Solutions

et al. 2022

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Communication systems and networks are evolving as an integral part of not only of our everyday life but also as a part of the industry, fundamental infrastructures, companies, etc. Current directions and concepts, such as the Internet of Things (IoT), promise enhanced quality of life, greater business opportunities, cost-effective manufacturing, and efficient operation management through ubiquitous connectivity and deployment of smart physical objects. IoT networks are able to collect, preprocess, and transmit a huge amount of data. A considerable portion of this data is security-and privacycritical data, which makes IoT networks a tempting option for attackers. Given that these networks deal with the actual aspects of our lives and fundamental infrastructures (e.g. smart grids), cybersecurity in such networks is crucial. The large scale of these networks and their unique characteristics and complexity bring further vulnerabilities. In this study we give an introduction to IoT, focusing on the IoT application layer, security requirements, threats, and countermeasures in this layer, and some of the open issues and future research lines.

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Section: ) Smart Agriculturementioning

confidence: 99%

Security in the Internet of Things Application Layer: Requirements, Threats, and Solutions

et al. 2022

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“…Los problemas relacionados con la agricultura obstaculizan continuamente los avances de los países. La única solución al inconveniente actual es una agricultura inteligente mediante la modernización [35]. De acuerdo con la Organización para la Cooperación y el Desarrollo Económicos-OECD [36], Colombia presenta una baja productividad en el sector agropecuaria, lo que restringe la competitividad del país [37].…”

Section: Gestión De La Agricultura 40 En Colombiaunclassified

Plataformas Tecnológicas en la Agricultura 4.0: una Mirada al Desarrollo en Colombia

Beltran

2022

CESTA

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Las Plataformas Tecnológicas son una de las herramientas más transcendentales para incrementar la competitividad de las industrias y, por ende, de un país, al generar innovación y la transferencia de tecnologías y generación de nuevos conocimientos. La presente revisión tiene como propósito identificar las principales características de las plataformas tecnológicas, relacionadas con la Agricultura que han sido desarrolladas en Colombia. En una primera fase el estudio analiza el marco conceptual de agricultura 4.0 y en una segunda fase se identifican las características, prácticas y aspectos relevantes de 3 Plataformas tecnológicas (PT), en las áreas de geo-informática, la variabilidad climática y desarrollo agrícola puestas a disposición, por la Corporación colombiana de investigación agropecuaria (AGROSAVIA). Esta investigación es abordada desde el método inductivo dado que este razonamiento es de gran utilidad, al establecer un vínculo de unión entre teoría y observación. Una primera etapa se desarrolló mediante el enfoque exploratorio-descriptivo y en una fase final se presenta la construcción de las características de cada una de las PT desarrolladas para el uso en la agricultura. Los procesos de investigación y de análisis da como resultado el establecimiento de los principales beneficios y retos de las Plataformas Tecnológicas para impulsar la agricultura.

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“…However, the traditional method of measuring the content of soil components is that for field sampling and then laboratory analysis. This method has high requirements for testers and requires expensive testing equipment [15]. Moreover, high testing cost, low efficiency, and inability to conduct large-scale testing simultaneously are its problems.…”

Section: B Problem Statementmentioning

confidence: 99%

Application of Hyperspectral Technology Combined With Bat Algorithm-AdaBoost Model in Field Soil Nutrient Prediction

et al. 2022

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This paper proposes a hyperspectral soil nutrient estimation method based on the bat algorithm (BA)-AdaBoost model. The spectral reflectance, the first derivative of the reflectance, and the reciprocal logarithm of the reflectance are analyzed based on the 800 field soil samples and their hyperspectral data collected. The first derivative of the reciprocal logarithm of the reflectance and the sensitive band was extracted using the correlation coefficient method, and the correlation of the content of soil organic matter, phosphorus, and potassium was solved. The BA is used to optimize the two core parameters of the AdaBoost model (i.e., the maximum number of iterations (n) and the weight reduction coefficient (v) of the weak learner), the classification and regression trees(CART) decision tree is selected as the weak regression learner of the model, and the coefficient of determination is used as parameter optimization. Based on the objective function value, a BA-AdaBoost model was constructed to estimate soil organic matter and phosphorus and potassium contents. The results show that the BA-AdaBoost combined model can better search for globally optimal parameters. The AdaBoost model optimized by BA significantly improved accuracy and reliability. Among the three elements, soil organic matter estimation accuracy is the highest, and the coefficient of determination and the root mean square error are 0.867 and 0.151g • kg -1 , respectively. Compared with the model before optimization, the model accuracy and reliability improved by 29.0% and 24.1%, respectively. The results indicate that hyperspectral technology combined with the BA-AdaBoost model has certain application prospects in field soil nutrient estimation.

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A Systematic Review on Monitoring and Advanced Control Strategies in Smart Agriculture

Cited by 98 publications

References 144 publications

Security in the Internet of Things Application Layer: Requirements, Threats, and Solutions

Security in the Internet of Things Application Layer: Requirements, Threats, and Solutions

Plataformas Tecnológicas en la Agricultura 4.0: una Mirada al Desarrollo en Colombia

Application of Hyperspectral Technology Combined With Bat Algorithm-AdaBoost Model in Field Soil Nutrient Prediction

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