On the Classification of a Greenhouse Environment for a Rose Crop Based on AI-Based Surrogate Models

A precise microclimate control for dynamic climate changes in greenhouses allows the industry and researchers to develop a simple, robust, reliable, and intelligent model. Accordingly, the objective of this investigation was to develop a method that can accurately define the most suitable environmen...

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Autores principales:	Showkat Ahmad Bhat, Nen-Fu Huang, Imtiyaz Hussain, Farzana Bibi, Uzair Sajjad, Muhammad Sultan, Abdullah Saad Alsubaie, Khaled H. Mahmoud
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	greenhouse microclimate Bayesian optimization deep neural network roses yield Gaussian process Environmental effects of industries and plants TD194-195 Renewable energy sources TJ807-830 Environmental sciences GE1-350
Acceso en línea:	https://doaj.org/article/66058b56520948d291590baf761c12e3
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spelling	oai:doaj.org-article:66058b56520948d291590baf761c12e32021-11-11T19:46:39ZOn the Classification of a Greenhouse Environment for a Rose Crop Based on AI-Based Surrogate Models10.3390/su1321121662071-1050https://doaj.org/article/66058b56520948d291590baf761c12e32021-11-01T00:00:00Zhttps://www.mdpi.com/2071-1050/13/21/12166https://doaj.org/toc/2071-1050A precise microclimate control for dynamic climate changes in greenhouses allows the industry and researchers to develop a simple, robust, reliable, and intelligent model. Accordingly, the objective of this investigation was to develop a method that can accurately define the most suitable environment in the greenhouse for an optimal yield of roses. Herein, an optimal and highly accurate BO-DNN surrogate model was developed (based on 300 experimental data points) for a quick and reliable classification of the rose yield environment considering some of the most influential variables including soil humidity, temperature and humidity of air, CO<sub>2</sub> concentration, and light intensity (lux) into its architecture. Initially, two BO techniques (GP and GBRT) are used for the tuning process of the hyper-parameters (such as learning rate, batch size, number of dense nodes, number of dense neurons, number of input nodes, activation function, etc.). After that, an optimal and simple combination of the hyper-parameters was selected to develop a DNN algorithm based on 300 data points, which was further used to classify the rose yield environment (the rose yield environments were classified into four classes such as soil without water, correct environment, too hot, and very cold environments). The very high accuracy of the proposed surrogate model (0.98) originated from the introduction of the most vital soil and meteorological parameters as the inputs of the model. The proposed method can help in identifying intelligent greenhouse environments for efficient crop yields.Showkat Ahmad BhatNen-Fu HuangImtiyaz HussainFarzana BibiUzair SajjadMuhammad SultanAbdullah Saad AlsubaieKhaled H. MahmoudMDPI AGarticlegreenhousemicroclimateBayesian optimizationdeep neural networkroses yieldGaussian processEnvironmental effects of industries and plantsTD194-195Renewable energy sourcesTJ807-830Environmental sciencesGE1-350ENSustainability, Vol 13, Iss 12166, p 12166 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	greenhouse microclimate Bayesian optimization deep neural network roses yield Gaussian process Environmental effects of industries and plants TD194-195 Renewable energy sources TJ807-830 Environmental sciences GE1-350
spellingShingle	greenhouse microclimate Bayesian optimization deep neural network roses yield Gaussian process Environmental effects of industries and plants TD194-195 Renewable energy sources TJ807-830 Environmental sciences GE1-350 Showkat Ahmad Bhat Nen-Fu Huang Imtiyaz Hussain Farzana Bibi Uzair Sajjad Muhammad Sultan Abdullah Saad Alsubaie Khaled H. Mahmoud On the Classification of a Greenhouse Environment for a Rose Crop Based on AI-Based Surrogate Models
description	A precise microclimate control for dynamic climate changes in greenhouses allows the industry and researchers to develop a simple, robust, reliable, and intelligent model. Accordingly, the objective of this investigation was to develop a method that can accurately define the most suitable environment in the greenhouse for an optimal yield of roses. Herein, an optimal and highly accurate BO-DNN surrogate model was developed (based on 300 experimental data points) for a quick and reliable classification of the rose yield environment considering some of the most influential variables including soil humidity, temperature and humidity of air, CO<sub>2</sub> concentration, and light intensity (lux) into its architecture. Initially, two BO techniques (GP and GBRT) are used for the tuning process of the hyper-parameters (such as learning rate, batch size, number of dense nodes, number of dense neurons, number of input nodes, activation function, etc.). After that, an optimal and simple combination of the hyper-parameters was selected to develop a DNN algorithm based on 300 data points, which was further used to classify the rose yield environment (the rose yield environments were classified into four classes such as soil without water, correct environment, too hot, and very cold environments). The very high accuracy of the proposed surrogate model (0.98) originated from the introduction of the most vital soil and meteorological parameters as the inputs of the model. The proposed method can help in identifying intelligent greenhouse environments for efficient crop yields.
format	article
author	Showkat Ahmad Bhat Nen-Fu Huang Imtiyaz Hussain Farzana Bibi Uzair Sajjad Muhammad Sultan Abdullah Saad Alsubaie Khaled H. Mahmoud
author_facet	Showkat Ahmad Bhat Nen-Fu Huang Imtiyaz Hussain Farzana Bibi Uzair Sajjad Muhammad Sultan Abdullah Saad Alsubaie Khaled H. Mahmoud
author_sort	Showkat Ahmad Bhat
title	On the Classification of a Greenhouse Environment for a Rose Crop Based on AI-Based Surrogate Models
title_short	On the Classification of a Greenhouse Environment for a Rose Crop Based on AI-Based Surrogate Models
title_full	On the Classification of a Greenhouse Environment for a Rose Crop Based on AI-Based Surrogate Models
title_fullStr	On the Classification of a Greenhouse Environment for a Rose Crop Based on AI-Based Surrogate Models
title_full_unstemmed	On the Classification of a Greenhouse Environment for a Rose Crop Based on AI-Based Surrogate Models
title_sort	on the classification of a greenhouse environment for a rose crop based on ai-based surrogate models
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/66058b56520948d291590baf761c12e3
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On the Classification of a Greenhouse Environment for a Rose Crop Based on AI-Based Surrogate Models

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