Ocimum Sanctum Linn Plant Growth Monitoring and Irrigation System

Abstract

Nowadays, the global challenge of ensuring food security for a growing population necessitates innovative solutions in agriculture. The convergence of Internet of Things (IoT) and Artificial Intelligence (AI) technologies presents a promising avenue for addressing key issues such as crop disease management and resource optimization. In that project proposes a holistic approach to smart agriculture by integrating IoT-based sensor systems for real-time monitoring of key agricultural parameters with AI-powered disease detection and severity estimation techniques. Leveraging advanced sensor technologies, including soil moisture, float level, pH, and humidity sensors, the proposed system collects real-time data from agricultural lands and transmits it to a cloud-based platform. Additionally, a deep learning-based Convolutional Neural Network (CNN) model is employed to detect and classify crop diseases from images captured in the field. The system further estimates disease severity by analyzing affected and unaffected leaf regions, enabling targeted treatment with appropriate pesticide concentrations. By combining IoT sensor data with AI-driven disease management. The integrated system offers a comprehensive solution for precision agriculture. Through early disease detection, optimized resource usage, and timely intervention, the proposed system aims to enhance crop yield, minimize wastage, and contribute towards global food security goals. © 2024 IEEE.