USING decision models to enable better irrigation Decision Support Systems

Car, Nicholas J.

doi:10.1016/j.compag.2018.07.024

Cited by 39 publications

(22 citation statements)

References 8 publications

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“…The development of useful new models based on the decision support system (DSS) has improved the automatic irrigation system [23][24]. An optimized DDS with a modified fertigation systems (DSS-FS) is designed primarily for drip and sprinkler irrigation, based on an environmental sustainability approach [25].…”

Section: Overview Of Drip Fertigation Methodsmentioning

confidence: 99%

Drip Irrigation Detection for Power Outage-Prone Areas with Internet-of-Things Smart Fertigation Managemant System

Dahnill¹,

Hood²,

Adam³

et al. 2021

IJACSA

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In drip irrigation agriculture or fertigation technique, sufficient amount of water and nutrients are crucial for a plant's growth and development. An electronic timer is usually used to control the plant watering automatically and the scheduling is set according to different levels of plant growth. The timer has to be adjusted frequently since the required amount of water is different according to the growth stages. In power outage-prone regions, the problem with scheduled irrigation using timer becomes worsen since the watering schedule is disrupted by occasional black-outs, leading to an insufficient supply of water and nutrients, which leads to poor crop yields. Typical solution for such problems is by hiring field works to monitor the functionality of the automated system, plant health, and to re-adjust the timer once a power outage occurs. However, this solution is ineffective, time-consuming, and acquires high overhead costs. This paper proposes a systematic irrigation method using the internet-of-things (IoT) framework in order to improve the monitoring of plant growth and consequently improves the efficiency of the workflow. This systematic fertigation monitoring system consists of power outage alerts, and on-line notifications of plant irrigation, pesticide delivery, and polybag cleaning schedule. As a result, by using the proposed system, higher efficiency in farming management is achieved, with a 40% reduction in manpower, as compared to a typical fertigation-based farming system. This system demonstrates greater control over irrigation scheduling, plant growth, recording of pesticide scheduling automatically, and polybag cleaning, all of which will improve crop yields significantly.

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Section: Overview Of Drip Fertigation Methodsmentioning

confidence: 99%

Drip Irrigation Detection for Power Outage-Prone Areas with Internet-of-Things Smart Fertigation Managemant System

Dahnill¹,

Hood²,

Adam³

et al. 2021

IJACSA

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“…Finally, a paper of Car [34] examined the willingness of farmers to make a shift in farming objectives and even structural changes in order to adopt new technologies.…”

Section: Literature Reviewmentioning

confidence: 99%

Innovation Model of Agricultural Technologies Based on Intuitionistic Fuzzy Sets

Stojić

Sedlak

et al. 2019

Sustainability

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The selection and rational use of mechanization significantly affects the cost of agricultural products. To achieve the best financial effects, it is necessary to optimize the use of existing machine parks. The authors suggest a decision tree for deciding whether to ‘innovate or not’. The aim of the research is to define an algorithm that determines whether or not the land is arable, and in this way to help the owner of the family farm in the planning of working hours for agricultural machines, i.e., managing the machine park. The lack of plans, which stems from the lack of accurate data on the appropriate conditions of cultivation, leads to inappropriate use of time and the capacity of the machine park. The decision process is split into four compound variables: biological conditions, economic environment, technological conditions, and expertise and workmanship quality. Linguistic values of these variables are modeled with intuitionistic fuzzy sets, allowing for imprecision in data as well as experts’ hesitation.

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“…For example, the MCDM methods were considered in [18]. Different scientists considered the theory of decision support systems [19][20][21] and their practical applications in many fields of human activity [22][23][24]. Decision-making methods develop dynamically [25][26][27].…”

Section: Introductionmentioning

confidence: 99%

“…Relation (17) was applied to archive the weighted-normalized Fuzzy Multi-Criteria Decision Making (FDMM) for WSM (Table 12), and relation (18) was applied to archive the weighted-normalized FDMM for WPM (Table 13). Relations (19) and (20), respectively, were applied to determine the values of the optimality function values for WSM and WPM. Table 13.…”

mentioning

confidence: 99%

A Fuzzy WASPAS-Based Approach to Determine Critical Information Infrastructures of EU Sustainable Development

et al. 2019

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Critical information infrastructure exists in different sectors of each country. Its loss or sustainability violation will lead to a negative impact on the supply of essential services, as well as on the social or economic well-being of the population. It also may even pose a threat to people’s health and lives. In the modern world, such infrastructure is more vulnerable and unstable than ever, due to rapid technological changes, and the emergence of a new type of threat—information threats. It is necessary to determine which infrastructure are of crucial importance when decision-makers aim to achieve the reliability of essential infrastructure. This article aims to solve the problem of ensuring the sustainable development of EU countries in terms of identifying critical information infrastructures. Integrated multi-criteria decision-making techniques based on fuzzy WASPAS and AHP methods are used to identify essential information infrastructures, which are related to a new type of potential threat to national security. The paper proposes a model for identifying critical information infrastructures, taking into account the sustainable development of countries.

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USING decision models to enable better irrigation Decision Support Systems

Cited by 39 publications

References 8 publications

Drip Irrigation Detection for Power Outage-Prone Areas with Internet-of-Things Smart Fertigation Managemant System

Drip Irrigation Detection for Power Outage-Prone Areas with Internet-of-Things Smart Fertigation Managemant System

Innovation Model of Agricultural Technologies Based on Intuitionistic Fuzzy Sets

A Fuzzy WASPAS-Based Approach to Determine Critical Information Infrastructures of EU Sustainable Development

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