Imagine a future where crop production no longer depends on favorable weather conditions but is precisely controlled by intelligent systems. As global population continues to grow, food security faces unprecedented challenges. Traditional agriculture, constrained by extreme weather, water scarcity and limited arable land, urgently needs breakthrough solutions.

Smart greenhouse technology has emerged as a viable solution, functioning as controllable "plant factories" that minimize environmental interference while providing optimal growing conditions. However, the true value of smart greenhouses lies in their ability to efficiently utilize energy and reduce operational costs.

The IoT Revolution in Agriculture

The integration of Internet of Things (IoT) technology enables precise greenhouse management. Networks of intelligent sensors monitor critical parameters including temperature, humidity, light intensity and CO2 concentration in real-time. This data facilitates intelligent decision-making to optimize resource allocation.

For instance, heating and cooling systems can be precisely adjusted according to environmental conditions and plant requirements, significantly reducing energy consumption. This approach not only enhances agricultural productivity but also delivers environmental benefits through reduced energy costs, creating an economic and ecological win-win scenario.

Modeling and Simulation for Optimization

Advanced modeling and simulation techniques provide deeper optimization capabilities for smart greenhouses. Computer models simulating crop growth under various environmental conditions allow farmers to predict climate change impacts and adjust cultivation strategies accordingly, improving yields while minimizing losses.

Current research focuses on developing autonomous greenhouse management systems that optimize plant growth while minimizing energy consumption. Temperature, humidity and light exposure constitute the key factors affecting greenhouse energy usage. Intelligent control systems maintain optimal growing conditions by dynamically adjusting these parameters.

Researchers employ multiple modeling approaches for smart greenhouse development. Physical models simulate internal greenhouse processes and evaluate different control strategies' energy impacts. Alternatively, machine learning models trained on historical environmental and plant growth data can predict optimal growing conditions.

Advanced Control Systems

Various control algorithms have been developed for greenhouse climate regulation, including Proportional-Integral-Derivative (PID) control, Model Predictive Control (MPC), optimal control, fuzzy logic, neural networks and hybrid algorithms. While PID control remains widely used for its flexibility and robustness, it faces challenges in handling external disturbances and nonlinear scenarios.

MPC methods have emerged as superior alternatives, capable of modeling nonlinear disturbances and constraints while predicting future events to adjust control measures accordingly. These systems have demonstrated effectiveness in optimizing water usage, regulating ventilation, managing CO2 concentration and controlling fan operation.

The DEVS Formalism Advantage

Discrete Event System Specification (DEVS) formalism offers unique advantages for modeling complex smart greenhouse systems. As one of the most powerful methods for discrete event systems, DEVS provides a proven formal foundation for modeling and simulation across various complex systems.

The proposed DEVS-based model simulates agricultural greenhouse behavior to balance energy consumption with required climate conditions (temperature, CO2 levels and humidity). This holistic approach could transform cultivation methods while ensuring efficient production with minimal environmental impact.

Smart greenhouses represent complex systems comprising multiple subsystems with numerous devices and sensors. DEVS formalism effectively describes each subsystem's behavior as an atomic model interacting with other components, while the entire system is represented as a coupled model capturing all interactions and dependencies.

Implementation and Results

The proposed system begins with sensor data collection monitoring the indoor environment. An artificial neural network (ANN)-based predictor then forecasts future weather conditions using both external meteorological data and internal sensor readings. An optimization technique determines optimal environmental parameters while considering user-defined preferences and energy consumption limits.

Simulation results across various scenarios demonstrate this novel approach significantly improves greenhouse energy efficiency. The integration of dedicated components for sensing, prediction, optimization and control, along with learning modules for predictors and optimizers, provides an effective framework for greenhouse environment management with minimized energy consumption.