Greenhouse Climate Control System Research Articles

Automated Hydroponic Farm System

Current agriculture faces a multitude of issues – limited seasons, excessive pesticide use, sustainability concerns in organic farming, high carbon footprint, and inconsistent produce quality. Traditional methods, restricted by seasons and harming the environment with pesticides, are struggling to keep up with a changing world. This paper proposes a groundbreaking solution: an automated hydroponic farm system. Unlike traditional methods, this system allows year-round, pesticide-free production through advanced growing stations using a precise nutrient delivery technique (NFT). To create perfect growing conditions, a Greenhouse Climate Control System with various sensors regulates factors like temperature, humidity, and light. Additionally, a Central Console acts as the brain of the system, connecting everything and monitoring the entire farm. By automating these processes, this hydroponic system aims to revolutionize agriculture, offering a sustainable, efficient, and high-quality alternative.

Application of Fractional-order PID controllers in a Greenhouse Climate Control System

A model-based Fractional Order Proportional-Integral-Derivative (FOPID) control design method is applied to a multivariable greenhouse climate control system. A multivariable transfer function model of the greenhouse system, obtained via model Identification experiment, provides plant’s model information for control design. The fractional controller is tuned using the conventional Biggest Log-modulus Tuning (BLT) method associated with integer order PID controllers. However, unlike the conventional PID case, the controller gains are first computed using internal model control design procedure. This produces preliminary values of controller parameters. The FOPID controller gains are further tuned using a single detuning factor (F) until a biggest log modulus of 2N dB is obtained where N is the number of loops in the MIMO system. Simulation studies, using MATLAB and Simulink, show that good compromise between performance and robustness is achievable for both temperature and humidity regulation in a greenhouse control application.

Machine Learning-Based Crop Stress Detection in Greenhouses.

Greenhouse climate control systems are usually based on greenhouse microclimate settings to exert any control. However, to save energy, water and nutrients, additional parameters related to crop performance and physiology will have to be considered. In addition, detecting crop stress before it is clearly visible by naked eye is an advantage that could aid in microclimate control. In this study, a Machine Learning (ML) model which takes into account microclimate and crop physiological data to detect different types of crop stress was developed and tested. For this purpose, a multi-sensor platform was used to record tomato plant physiological characteristics under different fertigation and air temperature conditions. The innovation of the current model lies in the integration of photosynthesis rate (Ps) values estimated by means of remote sensing using a photochemical reflectance index (PRI). Through this process, the time-series Ps data were combined with crop leaf temperature and microclimate data by means of the ML model. Two different algorithms were evaluated: Gradient Boosting (GB) and MultiLayer perceptron (MLP). Two runs with different structures took place for each algorithm. In RUN 1, there were more feature inputs than the outputs to build a model with high predictive accuracy. However, in order to simplify the process and develop a user-friendly approach, a second, different run was carried out. Thus, in RUN 2, the inputs were fewer than the outputs, and that is why the performance of the model in this case was lower than in the case of RUN 1. Particularly, MLP showed 91% and 83% accuracy in the training sample, and 89% and 82% in testing sample, for RUNs 1 and 2, respectively. GB showed 100% accuracy in the training sample for both runs, and 91% and 83% in testing sample in RUN 1 and RUN 2, respectively. To improve the accuracy of RUN 2, a larger database is required. Both models, however, could easily be incorporated into existing greenhouse climate monitoring and control systems, replacing human experience in detecting greenhouse crop stress conditions.

Non-linear control model for use in greenhouse climate control systems

In this study, a non-linear control system was designed and proposed to control the greenhouse climate conditions. This control system directly uses the information of sensors, installed inside and outside the greenhouse. To design this proposed control system, the principles of a non-linear control system and the concepts of equilibrium points and zero dynamics of system theories were used. To show the capability and applicability of the proposed control system, it was compared with an integral sliding mode controller. A greenhouse with similar climatic conditions was used to simulate the performance of the integral sliding mode controller. In this study, it was seen that the integral sliding mode control system was more accurate; however, the actuator signals sent by this control system were not smooth. It could damage and depreciate the greenhouse equipment more quickly than the proposed non-linear control system. It was also shown that the regulation of the temperature and humidity was performed very smoothly by changing the reference signals according to the weather conditions outside the greenhouse. The ability of these two control systems was graphically demonstrated for temperature and humidity responses as well as for the signals sent to the actuators.

Design and Robustness Analysis of Intelligent Controllers for Commercial Greenhouse

Abstract. In a commercial greenhouse, variables, such as temperature and humidity, should be controlled with minimal human intervention. A systematically designed climate control system can enhance the yield of commercial greenhouses. This study aims to formulate a nonlinear multivariable transfer function model of the greenhouse model using thermodynamic laws by taking into account the variables that affect the Greenhouse Climate Control System. To control its parameters, Mamdani model-based Fuzzy PID is designed which is compared with the performance of proportional-integral (PI) and proportional-integral-derivative (PID) controllers to achieve a smooth control action. The Fuzzy logic based PID provides robust control actions eliminating the need for conventional tuning methods. The robustness analysis is performed using values obtained from real-time implementation for the greenhouse model for Fuzzy based PID, PI and PID controllers by minimizing the Integral Absolute Error (IAE) and Integral Square Error (ISE). The greenhouse model has strong interactions between its parameters, which are removed by Relative Gain Array (RGA) analysis, thereby providing an effective control strategy for complex greenhouse production. Further, the stability analysis of non-linear greenhouse model is conducted with the help of the bode plot and Nyquist plot. Results show that good control performance can be achieved by tuning the gain parameters of controllers via step responses such as small overshoot, fast settling time, less rise time, and steady-state error. Also, smoother control action was obtained with Fuzzy based PID making the Greenhouse Climate Control System stable.

Sliding Mode Control Based on Disturbance Observer for Greenhouse Climate Systems

Greenhouses are closed environments that require careful climatic control, which can benefit from a system control method to cope with the high nonlinearity, complex coupling, and robustness of unknown disturbances. This paper presents a general framework for an integral sliding mode controller based on a disturbance observer combined with feedback linearization for a greenhouse temperature and humidity system. The first-principle greenhouse climate model is described as a standard affine nonlinear system. The feedback linearization control law is used to achieve a system consisting of two separate integrator channels for temperature and humidity. System compound disturbances are estimated by applying a sliding mode disturbance observer. Based on the observer, an integral sliding mode control is incorporated to enhance the robustness against uncertainties and guarantee satisfactory tracking performance even when there are unknown estimation errors. The validity and efficacy of the proposed control technique for greenhouse climate tracking were verified by comparison with simulation results obtained using the common sliding mode control method using feedback linearization without the disturbance observer. Based on this comparison, the developed controller shows a faster system response speed, higher control precision, and stronger anti-interference ability. This method can be applied to improve greenhouse climate control systems.

Multi-objective optimization and decision making for greenhouse climate control system considering user preference and data clustering

Optimization of the systems can increase their efficiency with appropriate system performance indices. Nowadays, climate control of industrial greenhouses consists of many control parameters such as light, temperature, CO$$_{2}$$2 concentration, humidity, and etc. Most of these systems use the PID control structure due to its simplicity, flexibility, and good performance. The electrical lamps and heaters can be used to provide appropriate light and temperature inside the greenhouse. Optimal tuning of electrical lamps and heaters control system has a significant influence on efficiency and performance improvement of greenhouse cultivation system. For this aim, NSGA-II, a well-known evolutionary algorithm, is applied for the system optimization verified with an exhaustive search approach. Making a final decision to choose the best solution among the optimal solutions is a challenging decision making issue. In this regard, post Pareto-optimal pruning algorithms are employed considering various user preferences and clustering approaches. The final results show and verify the substantial improvement of greenhouse climate control system efficiency and performance.

Greenhouse Climate Control and Monitoring System

Greenhouse Climate Control and Monitoring System

Reconfigurable adaptive fuzzy fault-hiding control for greenhouse climate control system

Modern greenhouses are equipped with various components for providing a comfortable climate for plant growth. A greenhouse component malfunction may significantly crumble the greenhouse performance and hence result in loss of production. Therefore, it is required to design a control system, which is stable, and is able to provide an acceptable performance even in a severe fault situation. In this paper, an active fault-tolerant control scheme to compensate for actuator and/or sensor faults in the greenhouse climate system is designed. The proposed control system consists of a self-tuned fuzzy PI control system, a sensitive and reliable observer-based fault detection and diagnosis (FDD) mechanism for different types of faults in the presence of system disturbances, and a robust fault-hiding-based reconfigurable control design able to compensate for faulty effects. In this approach, a set of virtual sensors and actuators are used to restore the closed-loop stability and to ensure accurate tracking of reference inputs. The efficiency of the proposed approach has been demonstrated through a set of simulation experiments on a nonlinear multi-input multi-output greenhouse climate system.

Reconfigurable adaptive fuzzy fault-hiding control for greenhouse climate control system

Reconfigurable adaptive fuzzy fault-hiding control for greenhouse climate control system

A Review of Greenhouse Climate Control and Automation Systems in Tropical Regions

Design and development of automation system for a tropical greenhouse involves different phases, including studying of environmental factors and crop responses, control algorithm, instrumentation and software/hardware interface. In contrast to cold arid climates, a tropical greenhouse is not to provide a warm and humid environment for crop, but to create an ideal condition in which plants can be protected against heavy rainfalls, direct sun radiation, disease, insects and birds. High relative humidity and ambient temperature climate in a tropical greenhouse creates a complicated dynamic system that is influenced by changes of external conditions, making it a challenging environmental control task. This paper reviews and addresses issues involved in the design procedure of automation control system in tropical greenhouses.

Development and Evaluation of A Comprehensive Greenhouse Climate Control System Using Artificial Neural Network

Development of controlled environment in greenhouse is of prim importance for out of season production, increasing yield and enhancing the quality of produce. Due to high cost and impossibility of continuous human attendance in greenhouse, it is desirable to control the greenhouse environment by employing automatic control devices. In This study, the greenhouse conditions were controlled by using artificial neural network (ANN). First, an experimental greenhouse was built and equipped with control instruments. Then by using electronic sensors, some climatic parameter data (temperature, humidity, carbon dioxide and light index) were measured and saved during five minute periods. In the next stage, three types of ANN including feed forward neural networks with multiple delays in the input, two-layer neural network with a feedback from hidden layer and input delay and three-layer neural network with two feedbacks from hidden layers and input delay were trained by 66% of the recorded data, and were evaluated by using the remaining data. The three-layer neural network with two feedbacks from hidden layers and input delay was able to better predict humidity and light index of the greenhouse with MSE,s of 0.025 and 0.032, respectively. Temperature and infrared index were better predicted by using the feed forward neural networks with multiple delays in the input with MSE,s of 0.016 and 0.017, respectively. In all cases, training time was less than 14 minutes and simulation time being always less than 0.2 second, makes using neural network feasible for automatic control of greenhouse.

Review. Advantages and disadvantages of control theories applied in greenhouse climate control systems

Today agriculture is changing in response to the requirements of modern society, where ensuring food supply through practices such as water conservation, reduction of agrochemicals and the required planted surface, which guarantees high quality crops are in demand. Greenhouses have proven to be a reliable solution to achieve these goals; however, a greenhouse as a means for protected agriculture has the potential to lead to serious problems. The most of these are related to the inside greenhouse climate conditions where controlling the temperature and relative humidity (RH) are the main objectives of engineering. Achieving appropriate climate conditions to ensure high yield and quality crops reducing energy consumption have been the objective of investigations for some time. Different schemes in control theories have been applied in this field to solve the aforementioned problems. Therefore, the objective of this paper is to present a review of different control techniques applied in protected agriculture to manage greenhouse climate conditions, presenting advantages and disadvantages of developed control platforms in order to suggest a design methodology according to results obtained from different investigations.

Nonlinear adaptive PID control for greenhouse environment based on RBF network.

This paper presents a hybrid control strategy, combining Radial Basis Function (RBF) network with conventional proportional, integral, and derivative (PID) controllers, for the greenhouse climate control. A model of nonlinear conservation laws of enthalpy and matter between numerous system variables affecting the greenhouse climate is formulated. RBF network is used to tune and identify all PID gain parameters online and adaptively. The presented Neuro-PID control scheme is validated through simulations of set-point tracking and disturbance rejection. We compare the proposed adaptive online tuning method with the offline tuning scheme that employs Genetic Algorithm (GA) to search the optimal gain parameters. The results show that the proposed strategy has good adaptability, strong robustness and real-time performance while achieving satisfactory control performance for the complex and nonlinear greenhouse climate control system, and it may provide a valuable reference to formulate environmental control strategies for actual application in greenhouse production.

RBF Network Based Nonlinear Model Reference Adaptive PD Controller Design for Greenhouse Climate*

This paper presents a model reference adaptive PD control scheme based on RBF neural network for the greenhouse climate control problem. A model of nonlinear conservation laws of enthalpy and matter between numerous system variables affecting the greenhouse climate is used to validate the proposed control scheme. Compared with the conventional adaptive PD control scheme based on RBF neural network, the proposed scheme has better adaptability, stronger robustness and set-point tracking performance for the complex and nonlinear time-varying greenhouse climate control system, and it may provide a valuable reference to formulate environmental control strategies for actual application in greenhouse production.

Multi-Objective Control Optimization for Greenhouse Environment Using Evolutionary Algorithms

This paper investigates the issue of tuning the Proportional Integral and Derivative (PID) controller parameters for a greenhouse climate control system using an Evolutionary Algorithm (EA) based on multiple performance measures such as good static-dynamic performance specifications and the smooth process of control. A model of nonlinear thermodynamic laws between numerous system variables affecting the greenhouse climate is formulated. The proposed tuning scheme is tested for greenhouse climate control by minimizing the integrated time square error (ITSE) and the control increment or rate in a simulation experiment. The results show that by tuning the gain parameters the controllers can achieve good control performance through step responses such as small overshoot, fast settling time, and less rise time and steady state error. Besides, it can be applied to tuning the system with different properties, such as strong interactions among variables, nonlinearities and conflicting performance criteria. The results implicate that it is a quite effective and promising tuning method using multi-objective optimization algorithms in the complex greenhouse production.

CHARACTERIZATION AND ANALYSIS OF THE EFFECTS OF GREENHOUSE CLIMATE CONTROL EQUIPMENT ON GREENHOUSE MICROCLIMATE AND CROP RESPONSE

An overview of the recent scientific and technical advances on issues concerning the greenhouse microclimate, control and management in Mediterranean regions is presented and discussed. Special emphasis is given on the effects of climate control equipment in: (a) greenhouse microclimate and (b) crop response. The main greenhouse climate control systems presented and discussed are the heating, dehumidification, ventilation, shading and cooling systems. These systems are the most widely used for greenhouse climate control especially in the Mediterranean area. In these areas, natural ventilation and whitewashing are the most common methods/systems used for greenhouse climate control during summer, since they require less energy, less equipment operation and maintenance and are much cheaper to install than other cooling systems. However, generally these systems are not sufficient for extracting the excess energy during sunny summer days and therefore, other cooling methods such as forced ventilation combined with evaporative cooling (mist or fog system, sprinklers, wet pads), are used. On the other hand, during the winter period, heating and dehumidification are necessary for a standard quality production. Optimal greenhouse climate control became more important in the latter part of the 20th century, when concerns about food safety, environmental pollution stimulated extensive research in the development of Integrated Production and Protection systems aiming at a significant reduction of pesticides use. The prospective and needs for future research and development in greenhouse climate control are presented in the conclusion. Special emphasis is given to the necessity to combine both physical and ecophysiological studies, because of the crucial role played by the crop in determining the microclimate in greenhouses.

A More Energy Efficient Controller for the Greenhouses Climate Control System

Environmental conditions such as the direct exposure of sensors to sunlight and the deterioration of connections between the sensors and the controllers risk the occurrence of noisy and incomplete measurements which may impair the greenhouse climate control system. Noisy measurements increase the number of false alarms received from fault detection and isolation (FDI) systems and makes it unreliable. The objective of this article was to reduce the measurement noise by using the system states estimated by an Extended Kalamn Filter (EKF) and Uncented Kalman Filter (UKF) instead of the noisy measured system states. The applied approach is not limited to greenhouses but it is applicable to any control system installed in a noisy environment. Results show that the proposed method successfully reduced the measurement noise and improved the energy efficiency of the greenhouse climate control.

Simplified architecture of a type-2 fuzzy controller using four embedded type-1 fuzzy controllers and its application to a greenhouse climate control system

Uncertainty is an inherent part of control systems used in real-world applications. The use of new methods for handling incomplete information and to cope with large amounts of uncertainties is of fundamental importance. The traditional type-1 fuzzy sets (T1FSs) used in conventional fuzzy logic systems (FLSs) cannot fully handle the uncertainties present in control systems. The type-2 FSs (T2FSs) used in T2FLSs can handle such uncertainties in a better way because they provide more parameters and more degrees of freedom. In spite of the features of the T2FS, it has not received the attention it deserves because of its implementation complexity. In this paper, a simple architecture of the T2FLS using four embedded T1FLSs is introduced. The performance of the proposed T2FLS is assessed by applying it to a multi-input multi-output (MIMO) greenhouse climate control system. Simulation results showed that the simplified architecture is able to cope with the complexity of the plant and has the ability to handle measurement and modelling uncertainties. The performance of the proposed T2FLS is comparable with the computationally costly T2FLS and outperforms its traditional T1FLS counterpart.

Methodic design of a measurement and control system for climate control in horticulture

Many papers describe applications of advanced controllers in greenhouses. As control literature focusses on control algorithms, the layout of the measurement and control system is usually underexposed. Unfortunately, commercially available greenhouse climate control systems do not have the necessary flexibility to accommodate these types of controllers. This paper describes a systematic approach to the design of a flexible measurement and control system, that can be adjusted to suit most control research in horticulture. Individual functions within a measurement and control system are identified and alternative solutions are given. The approach is applied to the design of the measurement and control system for the Watergy greenhouse. The controllers (in software) are versatile and setpoints can be generated by the easy-to-use user interface as well as by external software (like Matlab). Data is centrally stored and the system is easy to expand. It has been shown that the system has functioned robustly for the last 3 years. It has the capability to serve as basis for research and testing of various advanced control strategies in the Watergy system with adaptive model-based control as a final goal.