Intelligent Aeroponics System Using Machine Learning

Abstract

The Intelligent Aeroponics System Using Machine Learning is a cutting-edge agricultural technology that leverages the power of artificial intelligence to enhance the cultivation of plants in a soilless environment. This system integrates an array of sensors to collect data on crucial environmental parameters, plant health, and growth conditions. A machine learning model, trained on historical data, analyzes this information to make informed decisions in real-time. The system employs data- driven algorithms to optimize the delivery of nutrients, misting intervals, lighting conditions, and environmental control parameters. It includes a user-friendly interface for remote monitoring and control, enabling users to receive timely alerts and notifications about the system's status. Additionally, computer vision-based models are used to detect early signs of pests and diseases, allowing for swift intervention. With an emphasis on energy efficiency, security, and scalability, this intelligent aeroponics system offers a sustainable approach to crop cultivation. Its continuous feedback loop and data analysis ensure crop optimization and resource conservation. The system can be adapted to various plant types and growth stages, making it a valuable tool for modern agriculture. Overall, the Intelligent Aeroponics System Using Machine Learning not only maximizes crop yields but also minimizes resource consumption, enhancing the efficiency and sustainability of agricultural practices. Keywords: aeroponic, monitoring system, automated system, Arduino, IoT, machine learning, lettuce.