-Smart Agriculture Monitoring System Using CNN and IoT for Crop Disease Detection and Real-Time Environmental Analysis

Abstract

The increasing demand for precision agriculture has led to the integration of intelligent systems that combine sensor technology, artificial intelligence, and web-based interfaces to enhance crop management. This paper presents a Smart Agricultural Monitoring System that leverages Internet of Things (IoT) devices and Convolutional Neural Networks (CNN) to monitor environmental parameters and detect crop diseases in real time. The system comprises a network of sensors connected to a microcontroller to collect key environmental data such as soil moisture, temperature, humidity, and pH levels. Simultaneously, a CNN model is trained to classify crop diseases based on leaf images. A Flask-based web interface enables farmers to monitor sensor data, upload crop images, receive disease predictions, and obtain tailored recommendations. This integrated solution empowers farmers with timely insights, reduces crop losses, and enhances agricultural productivity. The proposed system demonstrates the effectiveness of combining AI and IoT for smart, scalable, and sustainable farming practices.