Smart Farming: Using IoT and AI to Improve Crop Yield in Aeroponics System

The increasing demand for fresh produce and sustainable food production has led to a surge in the popularity of indoor agriculture. Our paper presents an IoT-based aeroponics system that utilizes artificial light and an AI predictive model to optimize crop yield in indoor agriculture. The IoT sensors monitor environmental factors, and the artificial light source provides the optimal light spectrum and intensity for plant growth. By analyzing the data collected by the sensors, the AI predictive model utilizes machine learning algorithms to analyze and predict the crop yield. To check the effectiveness of the proposed system, we conducted a pilot study that demonstrated a 20% improvement in crop yield compared to traditional soil-based agriculture. Additionally, the system exhibited a higher growth rate and better control over the growing environment. Our research proposes a new approach that combines IoT, artificial light sources, and AI predictive models to improve crop yield and sustainability, showcasing the potential of IoT-based aeroponics systems to revolutionize indoor agriculture. This study's contribution to the field of indoor agriculture highlights the significant potential of the proposed system to improve indoor agriculture, and it can be applied in various settings such as urban agriculture and commercial greenhouses.