Smart Plant Health Monitoring System

Authors: Mukesh Sahni, Raj Mayaskar, Rajan Vankar, Yash Parikh, Vikram Kaushik

Abstract: This paper introduces a novel Smart Plant Health Monitoring System to identify and forecast plant health problems in real-time, facilitating data-driven decision-making for enhanced crop yield and sustainability. In contrast to conventional manual approaches, the system combines IoT sensors, cloud computing, and artificial intelligence to monitor environmental parameters like soil moisture, pH, temperature, and humidity constantly. Convolutional Neural Networks (CNN) are employed for plant disease identification in images, while sensor data is processed to provide an early warning for water stress or nutrient deficiencies. An easy-to-use web and mobile app, developed using Flask and Python, offers farmers actionable information. Automated irrigation monitoring and alert features are also integrated within the system to minimize wastage of resources and enhance crop management efficiency. With the integration of IoT-based sensing, machine learning, and real-time analytics, this product constitutes a major leap in precision agriculture, fostering sustainable agriculture and improved productivity.

DOI: https://doi.org/10.5281/zenodo.17320399