The Control of Greenhouses Based on Fuzzy Logic Using Wireless Sensor Networks

Alpay, Özlem; Erdem, Ebubekir

doi:10.2991/ijcis.2018.125905641

Cited by 20 publications

(10 citation statements)

References 20 publications

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“…This central node acts as a gateway and retransmits messages to a cloud-based application that controls an irrigation system using Sigfox. Also, in [ 107 ] the star topology is used to connect multiple sensors within a greenhouse. Such sensors use the ZigBee protocol to send messages to a central node, which acts as the network gateway.…”

Section: Discussionmentioning

confidence: 99%

“…Mesh networks are considered more complex to be implemented but also more reliable due to the redundancy of communication between the sensor nodes [ 105 ]. Such topology is used in [ 105 , 107 ] for monitoring a greenhouse. Tree (or cluster) networks combine multiple star networks.…”

Section: Discussionmentioning

confidence: 99%

“…Nevertheless, greenhouse management can be more complex. As shown in [104,107,120], greenhouse parameters like temperature and humidity are closely tied and changing one of them can affect several others.…”

Section: Processingmentioning

confidence: 99%

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A Systematic Review of IoT Solutions for Smart Farming

Navarro

Costa

Pereira

2020

Sensors

226

100

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The world population growth is increasing the demand for food production. Furthermore, the reduction of the workforce in rural areas and the increase in production costs are challenges for food production nowadays. Smart farming is a farm management concept that may use Internet of Things (IoT) to overcome the current challenges of food production. This work uses the preferred reporting items for systematic reviews (PRISMA) methodology to systematically review the existing literature on smart farming with IoT. The review aims to identify the main devices, platforms, network protocols, processing data technologies and the applicability of smart farming with IoT to agriculture. The review shows an evolution in the way data is processed in recent years. Traditional approaches mostly used data in a reactive manner. In more recent approaches, however, new technological developments allowed the use of data to prevent crop problems and to improve the accuracy of crop diagnosis.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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A Systematic Review of IoT Solutions for Smart Farming

Navarro

Costa

Pereira

2020

Sensors

226

100

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“…However, the crop yields were not made available in the paper. Similarly, Alpay and Erdem (2018) used fuzzy logic to optimise the sensors' operations to monitor soil moisture, temperature, lighting, and relative humidity in a greenhouse. A greenhouse prototype using acrylic and aluminium was built for the sensors and fuzzy logic control system.…”

Section: Related Workmentioning

confidence: 99%

Enhanced IoT-Based Climate Control for Oyster Mushroom Cultivation Using Fuzzy Logic Approach and NodeMCU Microcontroller

Ariffin¹,

Ramli²,

Zainol³

et al. 2021

JST

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Urban farming has the potential to utilise unused space in the community to alleviate food shortages and increase the community’s income through local food production. When Internet of Things (IoT) technology is integrated with urban farming, it can further improve its efficiencies and yield. The work in this paper improved our previous work of using an IoT-based climate control system to regulate the cultivation environment of oyster mushrooms automatically. Even though the climate control system could produce two batches of mushroom yields, there were several limitations, such as less efficient climate control due to threshold-based corrective action, water wastage, and system instability. This paper aims to address these stated limitations by implementing a fuzzy logic algorithm and redesigned the climate control system. Two crisp input variables from DHT22 sensors representing temperature and humidity were fed into the Node MCU microcontroller’s fuzzy logic coded in C language. The temperature and humidity conditions were divided into five fuzzy trapezoidal membership functions resulting in 25 fuzzy rules to control the duration of running the water pump and ventilation fan. An internal, lightweight web server were managed all HTTP client requests. The enhanced system also included a safety measurement to avoid overheating the microcontroller and causing water wastage. Upon analysis of the data captured in two months, the result showed a decrease of 40% in water utilisation and an increase of mushrooms yield up to 226%. The enhanced climate control system also facilitated maintaining and controlling the temperature and humidity conducive for optimal mushroom cultivation.

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“…Fuzzy systems are extremely effective in modeling complex systems and have the capacity to incorporate human expert knowledge even affected by uncertainty. In this respect, fuzzy systems have been implemented and applied in different domains of application such as industry [2], electronics [3], control systems [4], computing or mechanics [5,6], among many others.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Systems-as-a-Service in Cloud Computing

Parra-Royon¹,

Benítez²

2019

IJCIS

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Fuzzy systems have become widely accepted and applied in a host of domains such as control, electronics or mechanics. The software for construction of these systems has traditionally been exploited from tools, platforms and languages run on-premise computing infrastructure. On the other hand, rise and ubiquity of the cloud computing model has brought a revolutionary way for computing services deployment. The boost of cloud services is leading towards increasingly specific service offering just as data mining and machine learning service. Unfortunately, so far, no definition for fuzzy system as service is available. This paper identifies this opportunity and focus on developing a proposal for fuzzy system-as-a-service definition. To achieve this, the proposal pursues three objectives: the complete description of cloud services for fuzzy systems using semantic technology, the composition of services and the exploitation of the model in cloud platforms for integration with other services. As an illustrative case, a real-world problem is addressed with the proposed specification.

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The Control of Greenhouses Based on Fuzzy Logic Using Wireless Sensor Networks

Cited by 20 publications

References 20 publications

A Systematic Review of IoT Solutions for Smart Farming

A Systematic Review of IoT Solutions for Smart Farming

Enhanced IoT-Based Climate Control for Oyster Mushroom Cultivation Using Fuzzy Logic Approach and NodeMCU Microcontroller

Fuzzy Systems-as-a-Service in Cloud Computing

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