Heat Prediction and Control in Smart Phones Using Machine Learning

The project titled “Heat Prediction and Control in Smart Plants Using Machine Learning” focuses on developing an intelligent system to monitor and regulate environmental conditions for optimal plant growth. The system uses a DHT11 sensor to continuously measure temperature and humidity in the surrounding environment. These real-time data are analyzed using a machine learning model based on the Random Forest algorithm to predict future temperature trends with improved accuracy. An Arduino microcontroller acts as the central unit, interfacing with all components, including an LCD display for real-time status monitoring and a relay module for controlling connected loads. When the temperature exceeds a predefined threshold, the system automatically activates a DC water pump to circulate water through a soft copper tube, which helps in efficient cooling of the plant environment. This setup ensures better heat dissipation and uniform water distribution. The integration of prediction and automated control reduces manual intervention, optimizes resource usage, and enhances plant health, demonstrating the effectiveness of machine learning in smart agriculture systems.