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

**Showkat Ahmad Bhat 1,\* , Nen-Fu Huang 1,\*, Imtiyaz Hussain <sup>2</sup> , Farzana Bibi <sup>3</sup> , Uzair Sajjad 4,\*, Muhammad Sultan <sup>5</sup> , Abdullah Saad Alsubaie <sup>6</sup> and Khaled H. Mahmoud <sup>6</sup>**


**Abstract:** 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<sup>2</sup> 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.

**Keywords:** greenhouse; microclimate; Bayesian optimization; deep neural network; roses yield; Gaussian process; gradient boosting

**Copyright:** © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

## **1. Introduction and Motivation**
