Application analysis of ANFIS strategy for greenhouse climate parameters prediction: Internal temperature and internal relative humidity case of study

Hamidane, Hafsa; Faiz, Samira El; Rkik, Iliass; Khayat, Mohamed El; Guerbaoui, M.; Ed-Dhhak, Abdelali; Lachhab, Abdeslam

doi:10.1051/e3sconf/202129701041

Cited by 6 publications

(3 citation statements)

References 14 publications

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“…Main technique MSE [33] Fuzzy inference system 4.014e − 1 [27] Fuzzy inference system 1.200e − 2 [34] Fuzzy inference system 9.722e − 1 [35] Multiple linear regression ARIMA MLP feed-forward network 5.766e + 1 5.496e + 1 3.973e + 1 [36] CNN and GRU network 7.458e + 0 [37] RNN-LSTM 8.237e + 0 Mamdani fuzzy model developed 8.200e − 3…”

Section: Researchmentioning

confidence: 99%

Predictive Model of Humidity in Greenhouses through Fuzzy Inference Systems Applying Optimization Methods

Vanegas-Ayala

Barón-Velandia

Leal-Lara

2023

Advances in Fuzzy Systems

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Establishing the indoor and outdoor humidity values in a greenhouse allows us to describe the crop yield during its entire developmental cycle. This study seeks to develop a predictive model of indoor relative humidity values in a greenhouse with high accuracy and interpretability through the use of optimized fuzzy inference systems, in order to offer greenhouse users a clear and simple description of their behaviour. The three-phase methodology applied made use of descriptive statistics techniques, correlation analysis, and prototyping paradigm for the iterative and incremental development of the predictive model, validated through error measurement. The research resulted in six models which define the behaviour of humidity as a result of temperature, CO2, and soil moisture, with percentages of effectiveness above 90%. The implementation of a Mamdani-type fuzzy inference system, optimized by a hybrid method combining genetic and interior point algorithms, allowed to predict the relative humidity in greenhouses with high interpretability and precision, with an effectiveness percentage of 90.97% and MSE (mean square error) of 8.2e − 3.

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Section: Researchmentioning

confidence: 99%

Predictive Model of Humidity in Greenhouses through Fuzzy Inference Systems Applying Optimization Methods

Vanegas-Ayala

Barón-Velandia

Leal-Lara

2023

Advances in Fuzzy Systems

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“…The system achieved an availability percentage of 92%. The study conducted by [24] focused on predicting and managing temperature and humidity in greenhouses. In this study, the authors developed two ANFIS models to forecast the behavior of internal temperature and internal relative humidity based on historical data.…”

Section: Greenhouse Monitoring and Control Systemmentioning

confidence: 99%

Design and Remote Monitoring of a wireless-Controlled Smart Agricultural Greenhouse

Abou-Mehdi-Hassani,

Ait Bouh,

Boudnaya

et al. 2023

E3S Web of Conf.

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This article presents a wirelessly controlled smart agriculture system and offers a comprehensive literature review to explore advancements in greenhouse monitoring and control systems. The system utilizes various sensors and actuators to optimize climatic conditions and enhance the growth of Dianthus Caryophyllus (CLOVE), an aromatic plant. Developed using Internet of Things (IoT) technology, the smart greenhouse system continuously monitors crucial environmental variables, including temperature, humidity, CO2 levels, and soil moisture. The collected data is utilized by actuators to make real-time adjustments to the greenhouse environment. The prototype operates through wireless communication protocols accessible via a dedicated mobile application.

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“…For controlling various green houses, a fuzzy system and neural network techniques were used by researchers Koutb, M. et al [12], Lafont, F. et al [13], Marquez-Vera et al [14], Mote, T et al [15], Revathi, S et al [16], Xu, F. et al [17], Fourati, F. et al [18], Coelho, J. [19], Mohamed, S. et al [20], Atia, D. M. et al [21], Oubehar, H. et al [22], Hernández-Salazar et al [23], Qiuying, Z. et al [24], Khuntia, S. R. et al [25], and Hamidane, H.et al [26].…”

Section: Literature Reviewmentioning

confidence: 99%

A Novel Approach for an Outdoor Oyster Mushroom Cultivation using a Smart IoT-based Adaptive Neuro Fuzzy Controller

Dipali¹,

Subramanian²,

Kumaran³

2023

IJACSA

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An automatic environment control systems for greenhouses are turning to be very significant because of food demand, and rise in temperature and population of the world. This article proposes to design and implement a low cost, robust and water efficient autonomous smart internet of things (IoT) system to monitor and control the temperature, and humidity of an outdoor oyster mushroom growing unit. The IoT-based control system involves DHT22 sensors, ESP32 controller and actuators (water pump and cooling fan) to facilitate the adequate amount of air for circulation to maintain temperature and water to maintain humidity inside an outdoor oyster mushroom growing unit as per its requirement. A real working prototype is developed and implemented on integrating fuzzy inference system (FIS) in ESP32 controller using Arduino C with the help of its integrated design environment. The FIS is designed to calculate the switching on/off time of water pump and cooling fan on sensing current temperature, and humidity inside oyster mushroom unit with respect to ambient temperature, and humidity respectively. The prototype provides inside temperature, humidity, ambient temperature, ambient humidity, water pump time and fan time on Thing-speak platform in real time. Furthermore, the data is used for design and simulation of Adaptive Neuro Fuzzy Inference Controller for an outdoor oyster mushroom growing unit in MATLAB/Simulink to improve the performance of the system. The practical applicability of the proposed ANFIS controller over FIS Controller and industrial PID Controller is shown by simulation findings with use of experimental data. The system reduces water use as well as an extremely extraordinary administration required for monitoring the mushroom unit. In addition, it increases robustness of the system.

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Application analysis of ANFIS strategy for greenhouse climate parameters prediction: Internal temperature and internal relative humidity case of study

Cited by 6 publications

References 14 publications

Predictive Model of Humidity in Greenhouses through Fuzzy Inference Systems Applying Optimization Methods

Predictive Model of Humidity in Greenhouses through Fuzzy Inference Systems Applying Optimization Methods

Design and Remote Monitoring of a wireless-Controlled Smart Agricultural Greenhouse

A Novel Approach for an Outdoor Oyster Mushroom Cultivation using a Smart IoT-based Adaptive Neuro Fuzzy Controller

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