A new suggestion for an irrigation schedule with an artificial neural network

Karasekreter, Naim; Başçiftçi, Fatih; Fidan, Uğur

doi:10.1080/0952813x.2012.680071

Cited by 36 publications

(14 citation statements)

References 13 publications

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“…Karasekreter et al [122] implemented an ANN for scheduling irrigation in a strawberry orchard using soil moisture and its physical properties as model inputs. The system was able to achieve water savings of 20.5% and an energy saving of 23.9%.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

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: Artificial Neural Networkmentioning

confidence: 99%

Advanced Monitoring and Management Systems for Improving Sustainability in Precision Irrigation

et al. 2017

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“…In the last century, Hall and Nathan [8] proposed a dynamic programming model to solve the water allocation of supply, considering dollar benefits, cost, etc. They were followed by other researchers who proposed different intelligent algorithms to solve the optimal configuration problems [9][10][11][12][13][14][15]. However, an intelligent algorithm for the optimal solution of global water distribution is often difficult to achieve due to the complexity and variability of certain factors such as objective function and constraint conditions.…”

Section: Introductionmentioning

confidence: 99%

Study on Unsteady Flow Based on Optimized Water Distribution Model in Irrigation District

Chen

Han

et al. 2020

Sustainability

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The canal delivery system is the main infrastructure of agricultural irrigation. The efficiency of water use in agriculture can be achieved by mastering the dynamic process of unsteady flow in the channel. In this study, an unsteady flow model for the calculation of the water flow transition information of the river channel during the water distribution process was established, based on the water distribution scheme given by the backtracking-search algorithm (BSA). This model was more suitable for areas with inefficient channel systems. The research areas included the main irrigation channels in Xiying City, which is one of the typical agricultural areas in northwestern China. The scheme obtained by optimal solution proposed for Xiying Irrigation District was feasible. According to the results of the flow simulations, the sluice gate calculation correlation could determine the change process of the gate opening of each channel, which provided a basis to realize the modernization of the irrigation area.

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“…The study proved that ANN models provide the best estimates for pan evaporation. Karasekreter et al [46] used ANN to propose a new system of irrigation ratios and time intervals and proved that irrigation at night could reduce losses from evaporation and thereby promoting water conservation.…”

Section: Water Resources Applicationsmentioning

confidence: 99%

Applications of Soft Computing in Civil Engineering: A Review

Chandwani¹,

Agrawal²,

Nagar³

2013

IJCA

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The field of engineering is a creative one. The problems encountered in this field are generally unstructured and imprecise influenced by intuitions and past experiences of a designer. The conventional methods of computing relying on analytical or empirical relations become time consuming and labor intensive when posed with real life problems. To study, model and analyze such problems, approximate computer based Soft Computing techniques inspired by the reasoning, intuition, consciousness and wisdom possessed by a human beings are employed. In contrast to conventional computing techniques which rely on exact solutions, soft computing aims at exploiting given tolerance of imprecision, the trivial and uncertain nature of the problem to yield an approximate solution to a problem in quick time. Soft Computing being a multi-disciplinary field uses a variety of statistical, probabilistic and optimization tools which complement each other to produce its three main branches viz., Neural Networks, Genetic Algorithms and Fuzzy Logic. The review paper presents the applications of two major Soft Computing techniques viz., Artificial Neural Networks and Genetic Algorithms in the field of Civil Engineering, which to some extent has replaced the time consuming conventional techniques of computing with intelligent and time saving computing tools.

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A new suggestion for an irrigation schedule with an artificial neural network

Cited by 36 publications

References 13 publications

Advanced Monitoring and Management Systems for Improving Sustainability in Precision Irrigation

Advanced Monitoring and Management Systems for Improving Sustainability in Precision Irrigation

Study on Unsteady Flow Based on Optimized Water Distribution Model in Irrigation District

Applications of Soft Computing in Civil Engineering: A Review

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