Adaptive Neuro-Fuzzy Inference Systems for Modeling Greenhouse Climate

Charaf Eddine,Mohamed Mourad,Aissa Belmeguenai,Khaled Mansouri

doi:10.14569/ijacsa.2016.070114

Charaf Eddine, Mohamed Mourad + Show 2 more

Open Access

https://doi.org/10.14569/ijacsa.2016.070114

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Abstract

The objective of this work was to solve the problem of non linear time variant multi-input multi-output of greenhouse internal climate for tomato seedlings. Artificial intelligent approaches including neural networks and fuzzy inference have been used widely to model expert behavior. In this paper we proposed the Adaptive Neuro-Fuzzy Inference Systems (ANFIS) as methodology to synthesize a robust greenhouse climate model for prediction of air temperature, air humidity, CO2 concentration and internal radiation during seedlings growth. A set of ten input meteorological and control actuators parameters that have a major impact on the greenhouse climate was chosen to represent the growing process of tomato plants. In this contribution we discussed the construction of an ANFIS system that seeks to provide a linguistic model for the estimation of greenhouse climate from the meteorological data and control actuators during 48 days of seedlings growth embedded in the trained neural network and optimized using the back propagation and the least square algorithm with 500 iterations. The simulation results have shown the efficiency of the proposed model.

Highlights

In recent decades, a considerable effort was devoted to develop adequate greenhouse climate and crop models, for driving simulation, control and managing [1,2]
The present paper describes simulation results of an Adaptive Neuro-Fuzzy Inference Systems (ANFIS) system that seeks to provide a linguistic model optimized by back-propagation and the least square algorithm for predicting the greenhouse climate
We present the results of experiments and the comparison and analysis of results between the experimental and ANFIS model depending on the greenhouse internal climate parameters

Summary

Introduction

A considerable effort was devoted to develop adequate greenhouse climate and crop models, for driving simulation, control and managing [1,2]. The dynamic behavior of the internal microclimate of the greenhouse is a combination of physical processes involving energy transfer (radiation and heat) and mass balance (water vapor fluxes and CO2 concentration) [4]. These processes depend on the outside environmental conditions, structure of the greenhouse, type and state of the crop and on the effect of the control actuators (typically ventilating and heating to modify inside temperature and humidity conditions, shading and artificial light to change internal radiation, CO2 injection to influence photosynthesis and fogging/cooling for humidity enrichment). The main advantages of using automated climate control are energy conservation, better productivity, and reduced human intervention [5]

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