AI-Driven Phytoremediation: Predictive Modeling for Soil Contaminant Reduction

A serious and major environmental problem, soil pollution endangers human health, agricultural productivity, and ecosystems. Traditional methods, such as physical and chemical procedures, are typically costly, time-consuming, and may have unanticipated ecological effects. This initiative addresses these issues by introducing an AI-based phytoremediation technique that predicts the effectiveness of plant remediation in mitigating soil contamination using machine learning. Establishing a deep learning model that is trained on key environmental factors such as soil pH, moisture content, pollutant concentrations, and plant biomass is the main focus of this study. The model calculates the percentage reduction in contamination by phytoremediation based on an examination of these criteria, giving environmental scientists, agricultural experts, and policymakers crucial information. The deep learning model's hyperparameters were carefully adjusted to improve accuracy, resulting in consistent predicted performance across a range of soil types. A web-based PhytoremediationApp was developed using Flask for the backend and HTML, CSS, and JavaScript for the frontend for user convenience and simplicity of use. Users can enter soil data into the interactive system to receive dynamic visualisations, real-time predicted findings, and practical recommendations for effective soil management. The proposed framework not only increases the impact of phytoremediation but also paves the way for future advancements in AI-powered environmental sustainability by offering a technique of preventing soil pollution that is both scientifically and practically driven.