Thesis Open Access

A NEURAL NETWORK PREDICTION MODEL TO CONTROL THE INTERIOR OF GREENHOUSES

SAMUEL AYELE AYTENFSU


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    <subfield code="a">Asrat Mulatu (PhD)</subfield>
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    <subfield code="a">Mr. Tameru H/Sellasie</subfield>
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    <subfield code="a">&lt;p&gt;The main function of a greenhouse is climate adaptation by the side of the interior of the&lt;br&gt;
closed structure; it aims to produce a perfect condition for the development and growth of&lt;br&gt;
specific crops. It also ensures isolation and protection against harsh weather conditions&lt;br&gt;
and possible animal intrusions.&lt;br&gt;
This research proposed an Elman recurrent neural network prediction model to control&lt;br&gt;
the interior of greenhouses by considering four input parameters that influence the&lt;br&gt;
greenhouse. The input parameter considered is wind velocity,  concentration, outside&lt;br&gt;
temperature and humidity. This paper also considered the data acquisition technique for&lt;br&gt;
acquiring the parameter. The data used for developing the model also part of the research&lt;br&gt;
paper and acquired in the place of Addis Ababa by setting experiments in property of&lt;br&gt;
Ethiopian environmental and forest research institute.&lt;br&gt;
The data acquisition technique is very economical and done using the Arduino platform.&lt;br&gt;
The data after collected is filtered and normalized using the Min-Max normalization&lt;br&gt;
technique. Then scaled all the data to {0 1} and finally, the model is developed using&lt;br&gt;
Elman neural network. Several selection criteria for fixing the proper number of a hidden&lt;br&gt;
neuron are analyzed; 53 different criteria is tested based on evaluating the performance&lt;br&gt;
using different statistical error measurement. The design of a neural network is&lt;br&gt;
implemented based on these selection criteria using a convergence theorem. After&lt;br&gt;
implementation, to verify the effectiveness the proposed model simulation was conducted&lt;br&gt;
in real-time inside temperature and inside relative humidity greenhouse data. The result&lt;br&gt;
obtained a minimal MSE of 0.0218, MAE of 0.1016, and RMSE of 0.1477.&lt;br&gt;
The proposed model is simple, economical especially for developing countries, efficient&lt;br&gt;
for predicting with minimal error. These papers also include the comparison of other&lt;br&gt;
models with the proposed and summarized in the table&lt;br&gt;
&amp;nbsp;&lt;/p&gt;</subfield>
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