Fuzzy based Decision Support Model for Irrigation System Management

Mousa, Amthal K.; Croock, Muayad Sadik; Abdullah, Mohammed Najm

doi:10.5120/18230-9177

Cited by 19 publications

(11 citation statements)

References 9 publications

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“…Mousa and Abdullah [123] successfully applied a fuzzy logic model in scheduling irrigation in drip and sprinkler irrigation systems using ET, soil moisture data and crop growth stage as model inputs. Prakashgoud and Desai [124] employed a fuzzy logic system using soil moisture data, leaf wetness and climatological data as model inputs in order to implement irrigation scheduling decisions.…”

Section: Fuzzy Logicmentioning

confidence: 99%

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Advanced Monitoring and Management Systems for Improving Sustainability in Precision Irrigation

et al. 2017

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Globally, the irrigation of crops is the largest consumptive user of fresh water. Water scarcity is increasing worldwide, resulting in tighter regulation of its use for agriculture. This necessitates the development of irrigation practices that are more efficient in the use of water but do not compromise crop quality and yield. Precision irrigation already achieves this goal, in part. The goal of precision irrigation is to accurately supply the crop water need in a timely manner and as spatially uniformly as possible. However, to maximize the benefits of precision irrigation, additional technologies need to be enabled and incorporated into agriculture. This paper discusses how incorporating adaptive decision support systems into precision irrigation management will enable significant advances in increasing the efficiency of current irrigation approaches. From the literature review, it is found that precision irrigation can be applied in achieving the environmental goals related to sustainability. The demonstrated economic benefits of precision irrigation in field-scale crop production is however minimal. It is argued that a proper combination of soil, plant and weather sensors providing real-time data to an adaptive decision support system provides an innovative platform for improving sustainability in irrigated agriculture. The review also shows that adaptive decision support systems based on model predictive control are able to adequately account for the time-varying nature of the soil-plant-atmosphere system while considering operational limitations and agronomic objectives in arriving at optimal irrigation decisions. It is concluded that significant improvements in crop yield and water savings can be achieved by incorporating model predictive control into precision irrigation decision support tools. Further improvements in water savings can also be realized by including deficit irrigation as part of the overall irrigation management strategy. Nevertheless, future research is needed for identifying crop response to regulated water deficits, developing improved soil moisture and plant sensors, and developing self-learning crop simulation frameworks that can be applied to evaluate adaptive decision support strategies related to irrigation.

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Section: Fuzzy Logicmentioning

confidence: 99%

“…A fuzzy logic system is made up of a set used to classify input data into membership classes, a decision rule that is applied to each set which culminates in a human-like decision output from the system [123]. A detailed description of the process is given in [124].…”

Section: Fuzzy Logicmentioning

confidence: 99%

Advanced Monitoring and Management Systems for Improving Sustainability in Precision Irrigation

et al. 2017

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“…Another widely utilized rule-based model for irrigation management, with similar features to that of ANFIS, is a fuzzy logic system. Much success has been recorded in its practical implementation for the intelligent control of irrigation when integrated with WSN and IoT devices [114][115][116][117][118][119][120][121][122][123][124][125][126][127][128].…”

Section: Adaptive Neuro-fuzzy Inference System (Anfis)mentioning

confidence: 99%

Precision Irrigation Management Using Machine Learning and Digital Farming Solutions

et al. 2022

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Freshwater is essential for irrigation and the supply of nutrients for plant growth, in order to compensate for the inadequacies of rainfall. Agricultural activities utilize around 70% of the available freshwater. This underscores the importance of responsible management, using smart agricultural water technologies. The focus of this paper is to investigate research regarding the integration of different machine learning models that can provide optimal irrigation decision management. This article reviews the research trend and applicability of machine learning techniques, as well as the deployment of developed machine learning models for use by farmers toward sustainable irrigation management. It further discusses how digital farming solutions, such as mobile and web frameworks, can enable the management of smart irrigation processes, with the aim of reducing the stress faced by farmers and researchers due to the opportunity for remote monitoring and control. The challenges, as well as the future direction of research, are also discussed.

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“…This time window is fixed and was calculated by performing evaporation tests during the month of July in the town of Salamanca, Spain. It is possible to find literature on how to calculate time dynamically [ 44 , 45 ], however it is not the focus of this work.…”

Section: Monitoring Platform and Irrigationmentioning

confidence: 99%

Combining Multi-Agent Systems and Wireless Sensor Networks for Monitoring Crop Irrigation

Villarrubia

Paz

Iglesia

et al. 2017

Sensors

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Monitoring mechanisms that ensure efficient crop growth are essential on many farms, especially in certain areas of the planet where water is scarce. Most farmers must assume the high cost of the required equipment in order to be able to streamline natural resources on their farms. Considering that many farmers cannot afford to install this equipment, it is necessary to look for more effective solutions that would be cheaper to implement. The objective of this study is to build virtual organizations of agents that can communicate between each other while monitoring crops. A low cost sensor architecture allows farmers to monitor and optimize the growth of their crops by streamlining the amount of resources the crops need at every moment. Since the hardware has limited processing and communication capabilities, our approach uses the PANGEA architecture to overcome this limitation. Specifically, we will design a system that is capable of collecting heterogeneous information from its environment, using sensors for temperature, solar radiation, humidity, pH, moisture and wind. A major outcome of our approach is that our solution is able to merge heterogeneous data from sensors and produce a response adapted to the context. In order to validate the proposed system, we present a case study in which farmers are provided with a tool that allows us to monitor the condition of crops on a TV screen using a low cost device.

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Fuzzy based Decision Support Model for Irrigation System Management

Cited by 19 publications

References 9 publications

Advanced Monitoring and Management Systems for Improving Sustainability in Precision Irrigation

Advanced Monitoring and Management Systems for Improving Sustainability in Precision Irrigation

Precision Irrigation Management Using Machine Learning and Digital Farming Solutions

Combining Multi-Agent Systems and Wireless Sensor Networks for Monitoring Crop Irrigation

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