ADVANCED DRONE-BASED MONITORING OF AGRICULTURAL, FORESTRY, AND AQUATIC ECOSYSTEMS: TECHNICAL FRAMEWORK

The rapid advancement of drone technology has significantly transformed environmental monitoring, enhancing capabilities for observing and managing agricultural, forestry, and aquatic ecosystems. This paper presents a comprehensive technical framework for implementing advanced drone-based systems in...

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Veröffentlicht in:Journal of Engineering Science (Chişinău) Jg. 32; H. 2; S. 108 - 121
Hauptverfasser: Gutu, Maria, Rotaru, Lilia, Alexei, Victoria, Kapusteanski, Maxim
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Technical University of Moldova 15.07.2025
Schlagworte:
ai-driven analytics
drone-based monitoring
ecosystem management
precision agriculture
real-time data processing
ISSN:2587-3474, 2587-3482
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Zusammenfassung:The rapid advancement of drone technology has significantly transformed environmental monitoring, enhancing capabilities for observing and managing agricultural, forestry, and aquatic ecosystems. This paper presents a comprehensive technical framework for implementing advanced drone-based systems into ecosystem monitoring, focusing on integrating high-resolution sensors, data processing, and artificial intelligence-based analytics. The framework incorporates modern technologies, including drones from Da-Jiang Innovations or First-Person View drones equipped with metric cameras for aerial photogrammetry. These can be further enhanced with multispectral and Light Detection and Ranging sensors to acquire real-time data, enabling more effective analysis. Furthermore, the Proxmox Virtual Environment is the core of the system’s architecture, increasing effective virtualisation and deployment. Core data processing technologies include Python scripts, Quantum Geographic Information System, and Pix4D software for photogrammetric reconstruction, as well as Elasticsearch for database management, acquisition, and storage. The Kibana platform ensures interactive data visualisation and supports evidence-based decision-making. The service-oriented structure and system modularity enable the rapid integration of new analytical tools that are adaptable to diverse ecological contexts. Validation in operational environments confirms the framework’s ability to address challenges in ecosystem management, particularly in remote areas. This integrated approach contributes to more sustainable and adaptive ecosystem monitoring and management practices.
ISSN:2587-3474
2587-3482
DOI:10.52326/jes.utm.2025.32(2).10