IoT Sensor Network Approach for Smart Farming: An Application in Food, Energy and Water System

Mekonnen, Yemeserach; Burton, L. K.; Sarwat, Arif I.; Bhansali, Shekhar

doi:10.1109/ghtc.2018.8601701

Cited by 32 publications

(18 citation statements)

References 10 publications

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“…Overall, smart farming practices help in enhancing farm and production efficiency (O'Grady and O'Hare 2017), thereby minimising the supply-demand risks. Advantages of using smart farming practices and intelligent decision-making tools that go beyond the farm level are optimal resource allocation practices leading to reduced wastages (Wang, Lu, and Capareda 2020), reduce the environmental footprint and enhance sustainable agricultural practices (Kernecker et al 2020), reduce the energy and water footprints (Mekonnen et al 2018;Walter et al 2017) thereby improving food quality and safety (Kamble, Gunasekaran, and Sharma 2020) and enhancing the overall food security (Lioutas and Charatsari 2020).…”

Section: Managerial Implicationsmentioning

confidence: 99%

Agriculture supply chain risks and COVID-19: mitigation strategies and implications for the practitioners

Sharma

Shishodia

Kamble

et al. 2020

International Journal of Logistics Research and Applications

269

222

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The agricultural supply chains (ASCs) are exposed to unprecedented risks following COVID-19. It is necessary to investigate the impact of risks and to create resilient ASC organisations. In this study, we have identified and assessed the ASC risks caused by disruptions. These threats were assessed using Fuzzy Linguistic Quantifier Order Weighted Aggregation (FLQ-OWA). The findings reveal that supply risks, demand risks, financial risks, logistics and infrastructure risks, management and operational, policy and regulation, and biological and environmental risks have a significant impact in ASC depending upon the organisations scope and scale. Various strategies such as adoption of industry 4.0 technologies, supply chain collaboration and shared responsibility is identified for sustainable future. Theoretical and managerial implications are provided based on the outcomes of the study.

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Section: Managerial Implicationsmentioning

confidence: 99%

Agriculture supply chain risks and COVID-19: mitigation strategies and implications for the practitioners

Sharma

Shishodia

Kamble

et al. 2020

International Journal of Logistics Research and Applications

269

222

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“…Equation (1) shows the calculation of the potential evapotranspiration considering the stage of vegetative growth of the crop by weighting the potential evapotranspiration (Kc). -1 ].…”

Section: B Decision Rules Calculationsmentioning

confidence: 99%

“…The world population is increasing day by day and it is estimated to have 10 billion by 2050 [1]. Therefore, by the soaring global population, the need for food and water and energy are being increased as well [2].…”

Section: Introductionmentioning

confidence: 99%

Emulation of Energy Scheduling for an Agriculture Irrigation System Considering Field Data

Abrishambaf

Faria

Vale

2020

2020 IEEE International Conference on Environment and Electrical Engineering and 2020 IEEE Industrial and Commercial Power Syst

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As the global population is daily soaring, the need for water and energy is also increasing. This makes the role of the agriculture sector more tangible. Water and energy efficiency in this sector is still very low, and the need for smart management and strategic planning are obvious. This paper proposes an autonomous approach to increase the efficiency of energy and water consumption in the agriculture irrigation process. The model contains local renewable energy resources to supply the irrigation electricity demand. In the previous works developed by the authors, the irrigation scheduling had been performed by a decision tree approach. however, in this paper, the model schedules irrigation according to the real-time field data as well as the availability of the local resources to minimize operational costs. The system also includes a laboratory demonstration to test the scheduling process under practical challenges. A case study is also shown to validate the model, and its results show a gap between expected and real results. Nevertheless, the results proved the functionality and applicability of the proposed model.

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“…Therefore, to ensure optimal system operation, it is essential to implement a decision algorithm. The system's objective function for minimizing the EC is shown in Equation (1). In this objective function, P Buy is the power that the system purchases from the utility grid, and P Sell is the surplus of the generation that is injected in the utility grid.…”

Section: Decision-making Approachesmentioning

confidence: 99%

“…This, in turn, has led to greater energy and food demands. According to [1], the population is going to continue to soar, reaching somewhere around 10 billion by 2050. It is also expected that by 2035, global energy consumption is going to rise by up to 50% [2], the predicted consumption rise is largely attributed to the agriculture sector and the food supply chain.…”

Section: Introductionmentioning

confidence: 99%

Energy Scheduling Using Decision Trees and Emulation: Agriculture Irrigation with Run-of-the-River Hydroelectricity and a PV Case Study

et al. 2019

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Agriculture is the very backbone of every country. Unfortunately, agricultural sustainability is threatened by the lack of energy-efficient solutions. The threat becomes more evident with the constantly growing world population. The research community must, therefore, focus on resolving the problem of high energy consumption. This paper proposes a model of energy scheduling in agricultural contexts. Greater energy efficiency is achieved by means of PV (photovoltaics) and hydropower, as demonstrated in the conducted case study. The developed model is intended for contexts where the farm is located near a river, so the farmer can use the flowing water to produce energy. Moreover, the model has been emulated using a variety of state-of-the-art laboratory devices. Optimal energy scheduling is performed via a decision tree approach, optimizing the use of energy resources and reducing electricity costs. Finally, a realistic scenario is presented to show the technical features and the practical behaviors of each emulator when adapting the results of the decision tree. The research outcomes demonstrate the importance of the technical validation of each model. In addition, the results of the emulation reveal practical issues that had not been discovered during the theoretical study or during the simulation.

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IoT Sensor Network Approach for Smart Farming: An Application in Food, Energy and Water System

Cited by 32 publications

References 10 publications

Agriculture supply chain risks and COVID-19: mitigation strategies and implications for the practitioners

Agriculture supply chain risks and COVID-19: mitigation strategies and implications for the practitioners

Emulation of Energy Scheduling for an Agriculture Irrigation System Considering Field Data

Energy Scheduling Using Decision Trees and Emulation: Agriculture Irrigation with Run-of-the-River Hydroelectricity and a PV Case Study

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