Internet of Things (IoT) and Machine Learning based Optimized Smart Irrigation System

Royal, Neelisetty Sumadhur; Parre, Williams; Bandarupalli, Sreeram Chowdary; Achary, Murumalla Rabikanta; Jadala, Dr.Vijaya Chandra; Kavitha, M.

doi:10.1109/icssit55814.2023.10061080

Cited by 4 publications

(6 citation statements)

References 17 publications

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“…This survey provides valuable insight into the current situation and highlights the opportunity to improve current agricultural methods' effectiveness, sustainability, and adaptability to the changing agricultural landscape. Royal et al (2023) use IoT technology and machine learning algorithms to optimize their smart irrigation system. They use IoT sensors to collect environmental data and machine learning algorithms to improve irrigation schedules [9].…”

Section: Literature Surveymentioning

confidence: 99%

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Smart Health Monitoring System of Agricultural Machines: Deep Learning-based Optimization with IoT and AI

Shwetabh,

Ambhaikar

2024

BIO Web Conf.

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Implementing intelligent monitoring systems for Agricultural Machinery (AM) is hindered by the intricate and costly nature of the Internet of Things (IoT) sensor technologies. The heavy reliance on cloud and fog computing, the availability of network infrastructure, and the need for expert knowledge pose challenges in rural areas that lack network connectivity. Using edge devices, such as smartphones, which possess significant computational capabilities, is a potential solution that has not yet been fully realized in the commercial sphere. Furthermore, the increasing demand from users for economically viable and user-friendly technology serves as a driving force for transitioning away from expensive and intricate sensors towards more cost-effective alternatives. In the IoT era, there is anticipated to be a widespread network connection between a vast array of AM and service centers. Using smartphone applications has increased the potential for edge computation on smartphones to significantly aid in network traffic control. The development of an Artificial Intelligence (AI) - -based data analytic method poses a significant challenge due to the need to optimize for the limited computational capabilities of smartphones. However, the users’ demand for affordable technology renders it resistant to easy penetration. This paper uses IoT and AI to propose a Smart Health Monitoring System for Agricultural Machines with Deep Learning-based Optimization (SHMAM-DLO). This paper aims to propose a Fusion Genetic Algorithm (FGA) methodology and Artificial Neural Network (ANN) for optimization during monitoring the health of AM. The proposed approach enables cost-effective utilization of smartphone end devices by leveraging their built-in microphones instead of relying on expensive IoT sensors.

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Section: Literature Surveymentioning

confidence: 99%

“…Royal et al (2023) use IoT technology and machine learning algorithms to optimize their smart irrigation system. They use IoT sensors to collect environmental data and machine learning algorithms to improve irrigation schedules [9]. This demonstration shows sensor deployment in various fields and cloud-based analytics.…”

Section: Literature Surveymentioning

confidence: 99%

Smart Health Monitoring System of Agricultural Machines: Deep Learning-based Optimization with IoT and AI

Shwetabh,

Ambhaikar

2024

BIO Web Conf.

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“…Automation and monitoring technologies have garnered significant attention in recent years, offering promising solutions to enhance efficiency and productivity in agricultural practices [110,151,152]. These technologies, ranging from autonomous mobile platforms to sensor fusion systems, hold immense potential to revolutionize traditional farming methods by enabling real-time data collection, analysis, and decision-making.…”

Section: Automation and Monitoring In Agriculturementioning

confidence: 99%

“…Water scarcity and inefficient irrigation practices present significant challenges to agricultural sustainability [152,[163][164][165][166]. Precision irrigation systems and AI algorithms offer potential solutions to optimize water usage and improve crop yield.…”

Section: Water Management and Irrigation Efficiencymentioning

confidence: 99%

“…Precision irrigation systems and AI algorithms offer potential solutions to optimize water usage and improve crop yield. However, inadequate water governance frameworks and competing water demands from urbanization, industry, and ecosystem services exacerbate challenges in agricultural water management [152,160].…”

Section: Water Management and Irrigation Efficiencymentioning

confidence: 99%

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IoT Solutions with Artificial Intelligence Technologies for Precision Agriculture: Definitions, Applications, Challenges, and Opportunities

Senoo,

Anggraini,

Kumi

et al. 2024

Electronics

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The global agricultural sector confronts significant obstacles such as population growth, climate change, and natural disasters, which negatively impact food production and pose a threat to food security. In response to these challenges, the integration of IoT and AI technologies emerges as a promising solution, facilitating data-driven decision-making, optimizing resource allocation, and enhancing monitoring and control systems in agricultural operations to address these challenges and promote sustainable farming practices. This study examines the intersection of IoT and AI in precision agriculture (PA), aiming to provide a comprehensive understanding of their combined impact and mutually reinforcing relationship. Employing a systematic literature review following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines, we explore the synergies and transformative potential of integrating IoT and AI in agricultural systems. The review also aims to identify present trends, challenges, and opportunities in utilizing IoT and AI in agricultural systems. Diverse forms of agricultural practices are scrutinized to discern the applications of IoT and AI systems. Through a critical analysis of existing literature, this study contributes to a deeper understanding of how the integration of IoT and AI technologies can revolutionize PA, resulting in improved efficiency, sustainability, and productivity in the agricultural sector.

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Transforming Agriculture Through Internet of Things

Kulkarni,

Dandannavar,

Gokhale

2024

EAI/Springer Innovations in Communication and Computing

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Internet of Things (IoT) and Machine Learning based Optimized Smart Irrigation System

Cited by 4 publications

References 17 publications

Smart Health Monitoring System of Agricultural Machines: Deep Learning-based Optimization with IoT and AI

Smart Health Monitoring System of Agricultural Machines: Deep Learning-based Optimization with IoT and AI

IoT Solutions with Artificial Intelligence Technologies for Precision Agriculture: Definitions, Applications, Challenges, and Opportunities

Transforming Agriculture Through Internet of Things

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