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@article{176948,
        author = {Boorla Arjun and K Srilaxmi and Kamalesh and G Dhanushya},
        title = {AI-Driven Automated Hydroponic Nutrient Optimization System},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {11},
        pages = {7770-7781},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=176948},
        abstract = {The AI-powered hydroponic automation system optimizes plant growth and nutrient management through real-time monitoring and intelligent control. Using machine learning models trained on historical plant data and environmental conditions, the system predicts the ideal nutrient composition for different crops. Real-time sensor data from pH, TDS, EC, and temperature sensors, integrated with the ESP32 microcontroller, allows dynamic adjustment of nutrient proportions to maintain plant health. The system leverages image processing algorithms to detect nutrient deficiencies from user-uploaded plant images and provides corrective actions. A hybrid cloud and edge computing architecture ensures low-latency decision-making and secure data handling. User feedback after each crop cycle enhances the AI model's accuracy, improving efficiency over time. Separate containers for individual nutrients prevent chemical reactions, and automated pumps mix the solutions proportionally in the main reservoir. The system employs robust data encryption, role-based access control (RBAC), and anomaly detection for security and reliability. This innovative system enhances crop yield, reduces manual intervention, and adapts to climate changes, making it a sustainable and efficient solution for modern hydroponic farming.},
        keywords = {AI-powered hydroponics, nutrient optimization, machine learning, real-time monitoring, ESP32 microcontroller, image processing, cloud and edge computing, automated nutrient mixing, data security, crop yield enhancement.},
        month = {May},
        }