Fuzzy Greenhouse Temperature and Humidity Control based on Arduino

Benyezza, Hamza; Bouhedda, Mounir; Zerhouni, Mohamed Cherif; Boudjemaa, Mourad; Dura, Salam Abu

doi:10.1109/icass.2018.8652017

Cited by 12 publications

(3 citation statements)

References 11 publications

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“…Time-based and farmer-based methods do not use sensors' readings as they control the actuators according to scheduled time slots or instructions from farmers.Limit-based, the most common in with a predefined threshold. The output of the limit-based can either be binary (on/off) [204,226,[277][278][279][280][281], or more flexibly, fuzzy [194,266,[282][283][284][285][286][287][288][289][290][291]. Model-based algorithms take actions in accordance with a reference model, which can be based on scientific equations [101,292,293], or on previous data (i.e.…”

Section: B Autonomous Systemsmentioning

confidence: 99%

Technologies Driving the Shift to Smart Farming: A Review

Elbeheiry

Balog

2023

IEEE Sensors J.

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As today's agriculture industry is facing numerous challenges, including climate changes, encroachment of the urban environment and lack of qualified farmers, there is a need for new practices to ensure sustainable agriculture and food supply. Consequently, there is an emphasis on upgrading the farming practices by shifting towards Smart Farming (SF) -utilizing advanced information and communication technologies to improve the quantity and quality of the crop with minimal labor interference. SF has gained lots of interest in recent years utilizing a variety of technological innovations in the field, which imposes a challenge on farmers and technology integrators to identify the suitable technologies and best practices for a particular application. This paper provides a survey of the most recent SF scientific literature to identify common practices toward technology integration, challenges, and solutions. The survey was conducted on 588 papers published on the IEEE database following Cochrane methods to ensure appropriate analysis and interpretation of results. The papers' contributions were analyzed to identify necessary technologies that constitute SF, and consequently, research themes were identified. The identified themes are sensors, communication, big data, actuators and machines, and data analysis. Besides presenting an in-depth analysis of each identified theme, the paper discusses integrating more than one technology in systems to achieve independency. The most common SF systems are remote monitoring, autonomous, and intelligent decision-making systems.

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Section: B Autonomous Systemsmentioning

confidence: 99%

Technologies Driving the Shift to Smart Farming: A Review

Elbeheiry

Balog

2023

IEEE Sensors J.

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“…The air temperature inside the greenhouse should be controlled properly because an increase in air temperature before harvest can reduce fruit sweetness [22]. Many air temperature control systems, including RH control systems, have been developed for optimal plant growth, such as fuzzy control systems [23,24]. The weather data (air temperature, relative humidity, wind speed, and solar radiation) were then used to determine the reference evapotranspiration based on the following Penman-Monteith equation [25]:…”

Section: Micro-climate and Soil Moisture Monitoringmentioning

confidence: 99%

Functional Design of Pocket Fertigation under Specific Microclimate and Irrigation Rates: A Preliminary Study

et al. 2022

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Irrigation and fertilization technologies need to be adapted to climate change and provided as effectively and efficiently as possible. The current study proposed pocket fertigation, an innovative new idea in providing irrigation water and fertilization by using a porous material in the form of a ring/disc inserted surrounding the plant’s roots as an irrigation emitter equipped with a “pocket”/bag for storing fertilizer. The objective was to evaluate the functional design of pocket fertigation in the specific micro-climate inside the screenhouse with a combination of emitter designs and irrigation rates. The technology was implemented on an experimental field at a lab-scale melon (Cucumis melo L.) cultivation from 23 August to 25 October 2021 in one planting season. The technology was tested at six treatments of a combination of three emitter designs and two irrigation rates. The emitter design consisted of an emitter with textile coating (PT), without coating (PW), and without emitter as a control (PC). Irrigation rates were supplied at one times the evaporation rate (E) and 1.2 times the evaporation rate (1.2E). The pocket fertigation was well implemented in a combination of emitter designs and irrigation rates (PT-E, PW-E, PT-1.2E, and PW-1.2E). The proposed technology increased the averages of fruit weight and water productivity by 6.20 and 7.88%, respectively, compared to the control (PC-E and PC-1.2E). Meanwhile, the optimum emitter design of pocket fertigation was without coating (PW). It increased by 13.36% of fruit weight and 14.71% of water productivity. Thus, pocket fertigation has good prospects in the future. For further planning, the proposed technology should be implemented at the field scale.

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“…The Fuzzy Logic Control of Humidity and Temperature in Neonatal Incubator was studied by Ili Flores [16]. Temperature and Humidity Control of Fuzzy Greenhouses based on Arduino was investigated by Benyezza [17]. The design of a constant temperature boiler system with fuzzy control and remote monitoring functions was investigated by Chunli Jiang [18].…”

Section: Introductionmentioning

confidence: 99%

Temperature Monitoring System for Baby Incubator Based on Visual Basic

Latif

Arfianto

Poetro

et al. 2021

jrc

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An incubator room is designed to keep the temperature warm so that the baby feels comfortable. A temperature monitor system for the incubator room placed separately from where the officer works is a hassle and a waste of time. The research aimed to design a temperature detector for an infant incubator that had a constant temperature and was monitored directly in a staff room. An LM35 sensor as a temperature detector was used in the design and installed in a 100 x 80 cm room. The detected temperature was displayed on the LCD and computer in the staff room. The temperature (°C) was converted into an ADC (Analog Digital to Converter) value by an Arduino Uno microcontroller. Several LM35 sensors were mounted in rooms to detect the temperature. The LM 35 sensor was used by considering that it could be calibrated directly in the Celsius scale, a linear scale factor of 10mV/°C, and it had a temperature range between -55°C and 150°C.

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Fuzzy Greenhouse Temperature and Humidity Control based on Arduino

Cited by 12 publications

References 11 publications

Technologies Driving the Shift to Smart Farming: A Review

Technologies Driving the Shift to Smart Farming: A Review

Functional Design of Pocket Fertigation under Specific Microclimate and Irrigation Rates: A Preliminary Study

Temperature Monitoring System for Baby Incubator Based on Visual Basic

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