An IoT based Smart Irrigation Management System using Reinforcement Learning modeled through a Markov Decision Process

Campoverde, Luis Miguel Samaniego; Tropea, Mauro; Rango, Floriano De

doi:10.1109/ds-rt52167.2021.9576130

Cited by 15 publications

(8 citation statements)

References 10 publications

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“…Improved irrigation water use efficiency can be achieved by estimating the Crop Water Stress Index (CWSI) using the canopy temperature histogram produced from thermal infrared pictures acquired by UAVs (Bian et al, 2019). Therefore, the use of UAV-based remote sensing technology is a major advancement in smart irrigation management (Campoverde et al, 2021;Goap et al, 2018;Touil et al, 2022). The inefficiencies of usual irrigation techniques frequently lead to water waste and decreased agricultural productivity.…”

Section: Optical Sensors: Plant-based Irrigation Managementmentioning

confidence: 99%

Ontology-Based Smart Irrigation System: Enhancing Agricultural Water Management

Nisa,

Azam,

Rafiq

et al. 2024

ABBDM

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An Ontology-Based Smart Irrigation System is presented in this research with the goal of improving agricultural water management. The main issue discussed is the inefficiency of conventional irrigation techniques, which results in water waste and lower crop yields. The process used entails creating an ontology, which includes defining concepts, establishing relationships, identifying domains, and populating the ontology. In order to facilitate real-time monitoring and decision-making, the irrigation system also incorporates sensors, actuators, and data processing algorithms. The main conclusions show that the ontology-based approach boosts crop output, encourages sustainable farming practices, and enhances water consumption efficiency. The findings of this study imply that ontology-based smart irrigation systems present a viable way to deal with the problems associated with water scarcity, improve agricultural output, and reduce their negative effects on the environment. The research adds to the expanding corpus of information on intelligent irrigation systems and emphasizes the significance of implementing cutting-edge technologies for agriculture's sustainable water management.

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Section: Optical Sensors: Plant-based Irrigation Managementmentioning

confidence: 99%

Ontology-Based Smart Irrigation System: Enhancing Agricultural Water Management

Nisa,

Azam,

Rafiq

et al. 2024

ABBDM

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“…This paradigm shifts towards data-driven decision-making and not only boosts efficiency but also holds the potential to address the global challenges of food security in the face of growing population and changing climate conditions. The success of smart agriculture critically hinges on robust and high-bandwidth connectivity to facilitate seamless data transmission between various devices and central management systems [6][7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Optimizing bandwidth utilization and traffic control in ISP networks for enhanced smart agriculture

Hassan,

Abdelaal,

Oshaba

et al. 2024

PLoS ONE

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As the demand for high-bandwidth Internet connections continues to surge, industries are exploring innovative ways to harness this connectivity, and smart agriculture stands at the forefront of this evolution. In this paper, we delve into the challenges faced by Internet Service Providers (ISPs) in efficiently managing bandwidth and traffic within their networks. We propose a synergy between two pivotal technologies, Multi-Protocol Label Switching—Traffic Engineering (MPLS-TE) and Diffserv Quality of Service (Diffserv-QoS), which have implications beyond traditional networks and resonate strongly with the realm of smart agriculture. The increasing adoption of technology in agriculture relies heavily on real-time data, remote monitoring, and automated processes. This dynamic nature requires robust and reliable high-bandwidth connections to facilitate data flow between sensors, devices, and central management systems. By optimizing bandwidth utilization through MPLS-TE and implementing traffic control mechanisms with Diffserv-QoS, ISPs can create a resilient network foundation for smart agriculture applications. The integration of MPLS-TE and Diffserv-QoS has resulted in significant enhancements in throughput and a considerable reduction in Jitter. Employment of the IPv4 header has demonstrated impressive outcomes, achieving a throughput of 5.83 Mbps and reducing Jitter to 3 msec.

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“…This results in significant water savings, increased crop yield and reduced costs. WSNs can also help identify areas of the field that require more water, improving the overall efficiency of the irrigation system (Aldegheishem et al, 2022;Campoverde et al, 2021;Patel et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Enhancing the lifetime and performance of WSNs in smart irrigation systems using cluster‐based selection protocols

Hassan

Oshaba

El‐Emary

et al. 2023

Irrigation and Drainage

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This paper presents a comparative analysis of cluster‐based selection protocols for wireless sensor networks (WSNs) in smart irrigation systems. The study evaluates the performance of stable election protocol (SEP)‐based protocols, including conventional SEP, zonal SEP (ZSEP), enhanced SEP (ESEP) and threshold‐sensitive SEP (TSEP), and distributed energy‐efficient clustering (DEEC)‐based protocols, including conventional DEEC, developed DEEC (DDEEC), enhanced DEEC (EDEEC) and threshold‐sensitive DEEC (TDEEC). Simulation results show that TSEP and ZSEP outperform other SEP‐based protocols in terms of network lifetime, stability period, number of dead nodes and throughput. EDEEC and TDEEC show superior performance compared to other DEEC‐based protocols. However, TDEEC and EDEEC have a lower performance in terms of packets sent to cluster heads (CHs) due to the three levels of energy heterogeneity, which reduce the average packets sent to CHs. Finally, a comparative analysis between the SEP‐based and DEEC‐based protocols is presented. The findings reveal that TSEP outperforms all other protocols in terms of the performance metrics examined in this paper, except for throughput, which is higher in the case of ZSEP. Overall, the proposed cluster‐based selection protocols improve the energy efficiency and lifetime of WSNs in smart irrigation systems, and the results demonstrate their effectiveness in supporting the performance of smart irrigation systems.

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An IoT based Smart Irrigation Management System using Reinforcement Learning modeled through a Markov Decision Process

Cited by 15 publications

References 10 publications

Ontology-Based Smart Irrigation System: Enhancing Agricultural Water Management

Ontology-Based Smart Irrigation System: Enhancing Agricultural Water Management

Optimizing bandwidth utilization and traffic control in ISP networks for enhanced smart agriculture

Enhancing the lifetime and performance of WSNs in smart irrigation systems using cluster‐based selection protocols

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