Developing an Integrated AI Model for Predictive Maintenance in Hydroponics

This research explores how artificial intelligence can be integrated with hydroponic farming systems to enable predictive maintenance and reduce the risk of crop failure. As hydroponics becomes an increasingly important solution to global food security challenges, the study highlights the limitations of traditional maintenance approaches, which are either costly when performed frequently or risky when delayed. By examining how AI models can anticipate system failures before they occur, the paper aims to make hydroponic farming more reliable, scalable, and economically viable.

The study adopts a literature-driven and comparative analytical approach. It reviews existing research on AI applications in hydroponics, focusing on key operational factors such as nutrient levels, pH, electrical conductivity, environmental conditions, equipment health, and yield prediction. Various machine learning models including deep neural networks, random forests, support vector regression, and gradient boosting are evaluated based on their suitability for predicting specific parameters. Rather than promoting a single model, the research proposes a modular framework in which different AI models are assigned to the factors they predict best, reducing bias and improving overall system robustness.

The findings suggest that combining IoT-based sensing with specialized AI models can significantly enhance early detection of anomalies, optimize maintenance schedules, and prevent large-scale crop losses. The paper also highlights a key research gap: most existing studies address isolated components of hydroponic systems rather than offering a unified predictive maintenance framework. By synthesizing insights across multiple studies, this research proposes an integrated AI-driven approach that balances accuracy, scalability, and economic feasibility, paving the way for more resilient and intelligent hydroponic farming systems.