The project’s aim is twofold: a) to conduct research and develop advanced machine vision algorithms for remote monitoring of avifauna using thermal cameras and unmanned aerial vehicles (drones), and b) to design and develop an innovative system for the remote diagnosis of avian health and the timely warning of diseases occurrence. The Amvrakikos and Kerkini wetlands (wetlands under the Ramsar Convention), which are internationally recognised as biodiversity hotspots for their importance and their role in the conservation of endangered and protected bird species, are the target sites for the system development and evaluation.
In particular, the designed and developed machine vision algorithms will ensure precise remote observation without disturbing the bird colonies and will accurately identify the different species and habitats, which will be visualised in a geographic information system.
The research conclusions may be leveraged for the modernisation and improvement of the “Biosecurity Measures for Avian Influenza” of the Ministry of Rural Development and Food since, with the project implementation, the veterinary authorities will have the ability to be immediately informed about the occurrence of any suspected case. Therefore, they will have at their disposal a valuable tool to assess the risk and, in turn, inform those concerned of a potential disease outbreak in the area in time. The first crucial step in a comprehensive approach to managing and ensuring biosecurity involves the utilisation of modern equipment. This will enable early detection of bird diseases, allowing for timely implementation of containment measures to minimise the risk of introducing and spreading pathogens originating from wild birds to healthy populations.
The specific project will improve the process of disease detection before any mass mortality events occur, while immediate action can be taken to remove any dead birds to prevent spread. In addition, highly contagious bird diseases, which are characterised by a high mortality rate, can be detected in time.