Hybrid Digital Twin Model for Greenhouse and Underground Environments

The proposed hybrid digital twin model for greenhouse and underground environments integrates multiple environmental sensors to monitor key parameters such as soil moisture, light intensity (LDR), and pH levels. The collected sensor data is transmitted to a Python environment, where the Random Forest algorithm is applied to classify the data into good and bad conditions. Based on these classifications, appropriate actions are triggered through an Arduino system. For example, if the LDR intensity is low, the LEDs are activated; if the pH level is unfavourable or soil moisture is undetected, the pump is turned on to maintain optimal growing conditions. Conversely, when conditions are ideal—such as good pH levels and sufficient soil moisture—the pump remains off. Additionally, all sensor data is uploaded to Thing Speak for remote monitoring and analysis. This hybrid system ensures efficient and autonomous management of the greenhouse or underground environment, optimizing resource usage and promoting plant health.