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Synergistic Role of Artificial Intelligence and Machine Learning in Enhancing Hydrophyte-Based Heavy Metal Stress Management
Abstract
Heavy metal pollution threatens ecosystems and human health. Hydrophytes, with their natural ability to absorb and detoxify metals, are valuable for phytoremediation. However, their efficiency varies due to species differences and environmental factors. Integrating Artificial Intelligence (AI) and Machine Learning (ML) enhances this process by enabling predictive modeling, stress detection, and real-time monitoring. AI-driven tools optimize plant selection, growth conditions, and risk assessment while improving scalability and sustainability. Despite challenges like data quality, costs, and ethical concerns, the synergy of hydrophytes with AI/ML offers a promising, eco-friendly solution for restoring aquatic ecosystems.
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