Vol. 2, Issue 1, Part A (2025)

The growing urgency to monitor environmental changes in real time has revealed the limitations of conventional fixed and satellite-based observation systems, which often suffer from low spatial resolution, delayed data acquisition, and restricted accessibility. This research presents an integrated autonomous drone system powered by deep learning for real-time environmental monitoring across varied ecological settings, including urban-industrial, forest-canopy, and peri-urban water regions. The system combines adaptive flight planning, multisensor payloads, and onboard inference to enable dynamic data collection and rapid environmental analysis. Using convolutional neural networks and reinforcement learning-based mission optimization, the study achieved superior detection accuracy (F1-scores of 0.90-0.93) and reduced inference latency by approximately 35-40% compared to conventional decoupled approaches. Statistical analyses revealed significant improvements in sensor-ground data correlation (Pearson r > 0.9) and mission energy efficiency (8% reduction). These results confirm the hypothesis that coupling UAV autonomy with edge-based deep learning enhances the speed, accuracy, and reliability of environmental sensing. The proposed architecture successfully validated the feasibility of end-to-end UAV intelligence for pollution assessment, canopy health analysis, and water quality monitoring. The research also emphasizes the importance of FAIR data principles to ensure reproducibility and interoperability in future environmental applications. Practical recommendations derived from the findings advocate for the integration of such systems into environmental governance frameworks, capacity-building for UAV-based monitoring, and continued development of energy-efficient, AI-enabled drones. The integrated approach thus provides a scalable, adaptable, and sustainable technological solution for next-generation environmental monitoring and decision support.