# Optimizing Crop Yield and Reducing Energy Consumption in Greenhouse Control Using PSO-MPC Algorithm
## 1 Introduction  
Greenhouse cultivation has gained widespread popularity among crop growers due to its numerous benefits, including the extended growing season and protection of crops against temperature and weather changes, creating a safe environment for plant growth. In modern greenhouses, climatic conditions such as humidity, temperature, radiation, and carbon dioxide can be adjusted using a control system that regulates factors such as temperature, humidity, lighting, and air circulation to create optimal growing conditions for plants [1]. A variety of greenhouse control systems are available ranging from basic manual methods to advanced automated systems that utilize sensors, computers, and other electronic components to monitor and adjust growing conditions in real time [1]. Effective greenhouse control is critical for growers who require consistent, optimal growing conditions to produce high-quality crops. With the right greenhouse control methods and technologies, growers can improve crop yields, reduce waste, and minimize the impact of external factors such as weather changes on their operations.  
Greenhouse control algorithms are software programs integrated into greenhouse control systems that were designed to automate and optimize the management of environmental conditions within a greenhouse or other controlled growing environment. These algorithms utilize input from sensors that measure temperature, humidity, and light levels, and other factors, and perform real-time adjustments to environmental controls, such as heating, cooling, ventilation, and irrigation, to create optimal growing conditions for plants [2]. The development of greenhouse control algorithms has been greatly aided by advances in computer technology and the increasing availability of low-cost sensors and microcontrollers. These advancements have enabled growers to implement sophisticated control systems that respond quickly to changes in growing conditions and can be customized to meet the unique requirements of various plant species.  
Greenhouse control algorithms come in a variety of forms, ranging from simple, rule-based systems to more sophisticated machine learning and artificial intelligence (AI) algorithms. Some commonly used types include proportional-integral-derivative (PID) control algorithms [3], fuzzy logic control [4; 5; 6; 7], artificial neural networks (ANNs) [8; 9; 10], and model predictive control (MPC) algorithms [11; 12; 13]. These algorithms serve as an essential tool for growers seeking to optimize their crop yields and increase the efficiency of their operations. By leveraging the power of modern computing technology, growers can attain precise and effective control over the environmental conditions in their greenhouses, which leads to the production of higher-quality crops and more sustainable agricultural practices.  
The structure of this paper is shown below: A literature review of relevant works related to greenhouse control is given in Section 2. The proposed methodology, including the greenhouse climate model, biophysical model, and PSO-MPC control algorithm, is given in Section 3. Experimental results are provided in Section 4 to show the effectiveness of the proposed algorithm. Lastly, conclusions and future works are given in Section 5.