Model Predictive Control for Real-Time Irrigation Scheduling

Saleem, Syed K.; Delgoda, Dilini; Ooi, Su Ki; Dassanayake, K. B.; Liu, L; Halgamuge, Malka N.; Malano, Hector

doi:10.3182/20130828-2-sf-3019.00062

Cited by 48 publications

(23 citation statements)

References 27 publications

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“…The irrigation problem has input constraints in terms of optimal irrigation volume and output constraints in terms of soil moisture thresholds and the desired plant response to water deficits [135]. Ooi et al [139], Lozoya et al [101] and Saleem et al [135] described a model predictive control framework for irrigation scheduling based on a soil moisture balance model. They employed a system identification procedure to generate a grey box model of the soil-plant-atmosphere system with a network of soil moisture sensors providing real-time feedback to the control algorithm.…”

Section: Model Predictive Controlmentioning

confidence: 99%

“…The system performance is predicted over a finite horizon subject to constraints on both the inputs and outputs of the plant [101]. Readers are directed to [135][136][137] for an in-depth review of the theory of model predictive control and its application in various industries.…”

Section: Model Predictive Controlmentioning

confidence: 99%

“…It has been successfully implemented in the food industry, petrochemical industry and power generation among others [135]. Model predictive control employs a plant model and optimization algorithm to calculate plant inputs in order to achieve a future value of a performance criterion.…”

Section: Model Predictive Controlmentioning

confidence: 99%

See 2 more Smart Citations

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: Model Predictive Controlmentioning

confidence: 99%

Section: Model Predictive Controlmentioning

confidence: 99%

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Advanced Monitoring and Management Systems for Improving Sustainability in Precision Irrigation

et al. 2017

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“…The parameters of the continuous-time model are accurately estimated by optimization methods thus the proposed model is accepted as a base model for future control applications. The given mathematical model is a first-order auto-regressive model, introduced in [2][3][4][5], was used to describe the water balance in the soil of the root zone. Both inflows (irrigation and precipitation) and outflows (evapotranspiration, runoff and deep percolation) determine the amount of water in the soil.…”

Section: Modeling Of Irrigation Systemsmentioning

confidence: 99%

Modeling and Control of Irrigation Systems: A Short Survey and Future Directions

Çetin¹,

Beyhan²

2019

acperpro

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Modeling and control of irrigation systems has importance for the yield of the crops and especially, saving the water sources has prominent importance for the world. In this paper, recent modeling and control methods for the irrigation systems are surveyed for the future applications. Even though a mathematical model is constructed for the amount of water in soil, there are too many unpredictable disturbances on the irrigation systems so that the mathematical model cannot be exact. Therefore, the accuracy of the literature control methods is less than expected results. As a solution, the unknown model-based control methods and also accurate prediction methods are going to be promising approaches for irrigation systems in future.

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“…Previous work has focused on scheduling irrigation over long time frames such as seasonal water allocations. Real-time irrigation scheduling, for example, hourly or daily, has received little attention [40]. Olivier and Singels [41] found that rainfall data and other observations by farmers were often unreliable and propose to go toward a centralized data processing and model execution.…”

Section: Real-time Schedulingmentioning

confidence: 99%

On the Use of Decision-Support Tools for Improved Irrigation Management: AquaCrop-Based Applications

Wellens¹,

Raes²,

Tychon³

2017

Current Perspective on Irrigation and Drainage

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Feeding more people with less water is putting efficient irrigation practices worldwide high on the agendas. As a reaction, over the last decades, numerous irrigation decisionsupport tools have been developed. For several reasons, the gap between farmer and modeler remained in most cases too large. The Food and Agriculture Organization of the United Nations (FAO) contributes to alleviate the encountered adoption limitations with AquaCrop and its stand-alone AquaCrop plug-in. This simple and robust fieldcrop-water balance has been successfully tested for a wide range of crops and regions, and its database is still expanding through worldwide contributions. The present chapter describes how AquaCrop can help irrigation advisory services draft efficient irrigation calendars that are easily applicable and adoptable: either by the elaboration of site-specific irrigation schedule calendars in chart format when the user has no access to the needed data or by the integration of its plug-in in a server/client ICT application offering centralized data management. As for the irrigation charts, studies prove 10-30% water savings, while maintaining yield and requiring minimum data. The server/client application offers an all-in advice tool, including real-time irrigation advice and yield forecasts. No adoption assessments have yet been carried out, but several ongoing pilot studies are promising.

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Model Predictive Control for Real-Time Irrigation Scheduling

Cited by 48 publications

References 27 publications

Advanced Monitoring and Management Systems for Improving Sustainability in Precision Irrigation

Advanced Monitoring and Management Systems for Improving Sustainability in Precision Irrigation

Modeling and Control of Irrigation Systems: A Short Survey and Future Directions

On the Use of Decision-Support Tools for Improved Irrigation Management: AquaCrop-Based Applications

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