Plant Growth Light System Using STM32

This paper presents a Plant Growth Light System enhanced with AI-based plant health analysis to optimize plant growth and disease detection. The system utilizes an STM32 microcontroller, environmental sensors, and LED grow lights to regulate optimal lighting conditions. Additionally, an AI model integrated using Google Colab analyzes plant images to detect diseases and assess overall plant health. The combination of smart lighting control and AI-powered monitoring enhances agricultural efficiency by reducing manual intervention and improving plant yield. Experimental results show improved plant growth and high accuracy in disease detection, making this system a promising innovation for smart farming applications.