THE INTELLIGENT CONTROL OF THE CITY-FARM MICROCLIMATE BASED ON THE Q-LEARNING ALGORITHM
DOI:
https://doi.org/10.17721/AIT.2024.1.02Keywords:
distributed systems, IoT technologies, cloud computing, Q-learning, monitoring.Abstract
B a c k g r o u n d . In the context of the rapid development of urban farming and the growing interest in sustainable food production, microclimate management is becoming a key aspect to achieve optimal plant cultivation. Optimum management of temperature, humidity and light can help use limited space more efficiently, increasing yield per unit area. Climate control systems that allow you to create optimal conditions for plants allow you to increase production in a limited area. The purpose of the study is to make informed decisions in the climate control system based on reinforcement learning algorithms, in particular Q-learning, to increase the productivity and efficiency of growing microgreens in urban farming. M e t h o d s . In order to make informed decisions in the climate control system, the article examines the Q-learning algorithm, which consists of such stages as determining different climatic states of the system; selecting the action to be performed based on the current state of the system and a utility estimate that is calculated based on the Bellman equation. A microclimate management model was developed and implemented, which uses the Q-learning algorithm to optimize climate parameters. The research methodology included simulation of various environmental conditions, model training based on collected data and experimental testing in real conditions of urban farming. R e s u l t s . Experimental simulations using the Python programming language with TensorFlow, PyTorch and scikit-learn libraries confirmed the effectiveness of applying the Q-learning algorithm in the climate control system to increase the productivity and efficiency of growing microgreens. To ensure that the system has reached the desired state, strategies such as monitoring the actual parameter values using IoT sensors of the climate control system, analyzing the obtained Q-table values, and setting learning stopping criteria are used. The results of the program are transmitted to the actuators via the Wi-Fi data network using the ESP8266 microcontroller, which is used as a Wi-Fi module for the Arduino microcontroller. C o n c l u s i o n . The use of a climate control system with the Q-learning algorithm in urban farming contributes to the achievement of greater productivity, efficiency and stability of plant cultivation, which is reflected in the improvement of the results of plant cultivation.Downloads
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