DEVELOPMENT OF IMAGE PROCESSING BASED AI MODEL FOR DRONE BASED ENVIRONMENTAL MONITORING

Abstract

The rapid advancement of unmanned aerial systems and artificial intelligence has significantly enhanced the capabilities of environmental monitoring and assessment. This paper presents the development of an image processing–based AI model for drone-assisted environmental monitoring applications. The proposed system integrates high-resolution aerial imagery captured using drones with advanced image processing techniques and deep learning algorithms to detect, classify, and analyse environmental features in real time. The framework employs convolutional neural networks (CNNs) for feature extraction, object detection, and semantic segmentation to monitor parameters such as vegetation health, water body conditions, land-use changes, and pollution indicators. The system architecture consists of three primary modules: drone-based image acquisition, preprocessing and enhancement, and AI-based analysis. Image preprocessing techniques including noise reduction, contrast enhancement, and colour space transformation are applied to improve data quality. The trained AI model processes the enhanced images to identify environmental anomalies and generate geospatial insights. Performance evaluation is conducted using metrics such as accuracy, precision, recall, and F1-score to validate the robustness of the model. The developed model enables efficient, cost-effective, and scalable environmental monitoring compared to traditional ground-based methods. By combining drone technology with intelligent image processing, the system supports applications in agriculture monitoring, forest management, pollution detection, and disaster assessment. The proposed approach demonstrates high reliability and real-time adaptability, making it a promising solution for sustainable environmental management and smart ecological surveillance systems.