AI-powered automated hydroponic system for smart agriculture.

This research presents an AI-powered automated hydroponic system designed to enhance the efficiency and sustainability of modern agriculture. The system integrates real-time environmental monitoring, automated nutrient management, and AI-based disease detection to optimize plant growth and minimize manual intervention. An ESP32 microcontroller collects data from specialized sensors measuring Total Dissolved Solids (TDS), pH, temperature, and light intensity. Data is wirelessly transmitted via MQTT to an EMQX broker, subsequently processed by an ExpressJS backend, and stored in a Firebase Realtime Database. A NextJS web application provides a user-friendly dashboard for visualization, alerts, and remote control. Automation is achieved using relay-controlled peristaltic and water pumps that adjust nutrient dosing and circulation based on sensor readings. A camera module captures plant images, which are analyzed by a CNN model running on a separate AI server to detect common spinach diseases like Anthracnose and Downy Mildew, enabling early intervention. This integrated system combines IoT, cloud data management, automation, and AI-based visual inspection to offer a comprehensive solution for precision hydroponic farming. Evaluation demonstrates high accuracy in disease detection, robust system performance, and significant potential for improving crop health, yield, and reducing manual labor in diverse agricultural settings. The system, along with its full codebase, has been made publicly available to promote reproducibility.• • •