NABATEQ™: The Real Time Smart Plant Health Monitoring System

This paper presents the development and implementation of an advanced plant health monitoring system. The primary objective is to create an integrated system capable of assessing and monitoring plant health by incorporating various environmental factors. Our approach addresses critical elements necessary for plants' well-being, including air temperature, soil moisture, soil temperature, soil conductivity, potential of Hydrogen values (pH), water levels, humidity, and the presence of essential nutrients such as Nitrogen, Phosphorus, and Potassium. The core of our methodology is the application of computer vision utilizing a night vision camera to assess the health of a plant. The captured images are compared against a reference database containing various health statuses. This comparative analysis is facilitated by an AI deep learning model, allowing for accurate assessments of plant health. Additionally, we have developed a user-friendly interface application that provides easy and remote access to the entire system. Deploying this data-driven approach within cloud-connectivity, our system aims to deliver precise and timely insights into plant health, offering a valuable tool for effective plant care and management.