A Model-Based Real-Time Decision Support System for Irrigation Scheduling to Improve Water Productivity

Chen, Xiaoping; Qi, Zhiming; GUI, Dongwei; Gu, Zhe; Ma, Liwang; Zeng, Fanjiang; Li, Lanhai; Sima, Matthew

doi:10.3390/agronomy9110686

Cited by 29 publications

(23 citation statements)

References 47 publications

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“…The soil moisture content is used to describe the water status in the uppermost part of a field soil [6]. The determination of soil moisture status has been considered regarding plant-water relations [7], while the climatic data are considered to perform a model-based real-time decision support system for irrigation systems together with the soil moisture status, such as air temperature, air humidity, solar radiation, and wind speed [8]. Furthermore, the smart watering system was developed for irrigation scheduling based on Block-chain and fuzzy logic approach by employing economical sensor devices [9].…”

Section: Introductionmentioning

confidence: 99%

An Intelligent Irrigation Scheduling System Using Low-Cost Wireless Sensor Network Toward Sustainable and Precision Agriculture

et al. 2020

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Agricultural irrigation developments have gained attention to improve crop yields and reduce water use. However, traditional irrigation requires excessive amounts of water and consumes high electrical energy to schedule irrigations. This paper proposes a fuzzy-based intelligent irrigation scheduling system using a low-cost wireless sensor network (WSN). The fuzzy logic system takes crop and soil water variabilities into account to adaptively schedule irrigations. The theoretical crop water stress index (CWSI) is calculated to indicate plant water status using canopy temperature, solar irradiation, and vapor pressure deficit. Furthermore, the soil moisture content obtained by a capacitive soil moisture sensor is used as a determination of water status in soil. These two variables are thus incorporated to improve the precision of the irrigation scheduling system. In the experiment, the proposed irrigation scheduling system is validated and compared with existing conventional irrigation systems to explore its performance. Implementation of this system leads to a decrease in water use by 59.61% and electrical energy consumption by 67.35%, while the crop yield increases by 22.58%. The experimental results reveal that the proposed irrigation scheduling system is effective in terms of precision irrigation scheduling and efficient regarding water use and energy consumption. Finally, the cost analysis is performed to confirm the economic benefit of the proposed irrigation scheduling system. INDEX TERMS Crop water stress index (CWSI), Fuzzy logic system, Irrigation scheduling, Wireless sensor network (WSN), Soil moisture content.

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Section: Introductionmentioning

confidence: 99%

An Intelligent Irrigation Scheduling System Using Low-Cost Wireless Sensor Network Toward Sustainable and Precision Agriculture

et al. 2020

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“…In their studies, Chen et al (2019) highlighted that a precisely timed irrigation program to meet crop water demand is vital to improving water use efficiency in arid farmlands. A real-time irrigation planning infrastructure was created, and DSS was developed for irrigation planning [26]. Bomsdorf and Derigs (2008) developed a model to solve the filmmaking planning problem in their work.…”

Section: Literature Reviewmentioning

confidence: 99%

Developing a Decision Support System for Exam Scheduling Problem Using Genetic Algorithm

Doğan

Yurtsal

2021

Eskişehir Technical University Journal of Science and Technology a - Applied Sciences and Engineering

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Exam scheduling is a very complex process done every semester in every educational institution and is usually done manually. The limited resources in these institutions make the preparation of the exam program a demanding and inconvenient task. In addition, when exam scheduling is examined in detail, it is seen that it is a comprehensive task that requires fulfillment of many situations other than the appointment of the appropriate course for the appropriate time period. In this study, a solution approach that allows the assignment of courses to time periods is proposed for the educational institution whose data we use. Thus, it is aimed to obtain a program that can quickly solve the exam scheduling problem, which is a comprehensive task for the educational institution. A Genetic Algorithm, an artificial intelligence optimization algorithm, was used as a solution method, and the performance of the method on the problem was tested. In addition, taking into account students, lecturers, and administrative staff's performance within the study's scope and purpose, it aims to obtain exam programs that will satisfy everyone. However, most studies focused on the solution to the problem, and the idea of making this program suitable for the use of non-technical personnel was neglected. The program we developed in this study has been turned into a decision support system. Thus, the program has become a structure suitable for the use of non-technical personnel. As a result of the study, considering the institution's entire structure, a program that the institution can use in every exam period was obtained, and the exam program was automated, eliminating the time and effort spent by the institution staff.

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“…Results from the SWAT model, which was calibrated using remotely sensed (RS) evapotranspiration data before being implemented for BMP scenario evaluation, demonstrate the efficacy of the tool to inform watershed managers of the potential benefits of various BMP scenarios in reducing sediment yield and N/P loads to streams. Chen and coauthors [13] evaluated a real-time decision support system for irrigation scheduling (DSSIS) which was built with an agro-hydrological model. When applied to a cotton field, the authors found that the DSSIS performed better than other methods that were evaluated concurrently.…”

Section: Csms As a Decision Toolmentioning

confidence: 99%

Model Application for Sustainable Agricultural Water Use

Jeong

2020

Agronomy

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With the growing population and climate change, increasing demands for water are intensifying competition between agricultural stakeholders. Since the mid-20th century, numerous crop models and modeling techniques have emerged for the quantitative assessment of cropping systems. This article introduces a collection of articles that explore current research in model applications for sustainable agricultural water use. The collection includes articles from model development to regional and field-scale applications addressing management effects, model uncertainty, irrigation decision support systems, and new methods for simulating salt balances. Further work is needed to integrate data science, modern sensor systems, and remote sensing technologies with the models in order to investigate the sustainability of agricultural systems in regions affected by land-use change and climate change.

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A Model-Based Real-Time Decision Support System for Irrigation Scheduling to Improve Water Productivity

Cited by 29 publications

References 47 publications

An Intelligent Irrigation Scheduling System Using Low-Cost Wireless Sensor Network Toward Sustainable and Precision Agriculture

An Intelligent Irrigation Scheduling System Using Low-Cost Wireless Sensor Network Toward Sustainable and Precision Agriculture

Developing a Decision Support System for Exam Scheduling Problem Using Genetic Algorithm

Model Application for Sustainable Agricultural Water Use

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