| 1- "AI and Machine Learning for Integrated Natural Ecosystem Health Monitoring": This research develops AI-driven solutions to assess ecosystem health through integrated analysis of multi-source data (mobile, drone, and satellite imagery). Using computer vision (OpenCV, PyTorch) and machine learning (CNNs, ViTs, scikit-learn), we extract key ecological indicators like vegetation cover, biomass, and soil health. Our pipeline combines high-resolution drone data for local precision with satellite imagery for broad-scale trends, enabling scalable habitat monitoring. Deep learning models automate species identification and biomass estimation, while fusion techniques correlate ground-truth data with remote sensing. The system delivers cost-effective, real-time insights through deployable dashboards, supporting evidence-based conservation. By bridging field ecology with AI, we create adaptive tools for biodiversity preservation and climate resilience. This interdisciplinary approach advances precision ecology through innovations in data fusion, anomaly detection, and predictive modeling for sustainable ecosystem management. |