EcoSonicML: Harnessing Machine Learning for Biodiversity Monitoring in South African Wetlands

Abstract

Biodiversity monitoring, particularly in a country as diverse as South Africa with its extensive migratory bird population, presents significant challenges. This challenge becomes even more pronounced in environments with a multitude of coexisting bird species, notably wetlands, which serve as crucial breeding and feeding grounds for various avian species. This research will address this challenge by designing a cost-effective sound-based sensor system capable of deployment in diverse wetland ecosystems. The primary aim is to aid in the monitoring of bird species by detecting their presence and distribution and then transmitting this valuable data to a central base station. To assess the system’s feasibility and performance, a series of experiments were conducted at the Rondevlei Nature Reserve in Cape Town, South Africa. These experiments focused on the sensor’s capacity to accurately identify avian species while maintaining robustness in varying environmental conditions. The results yielded promising outcomes, demonstrating the successful identification of bird species. Furthermore, the system exhibited reliability across different weather conditions, positioning it as a viable choice for long-term deployment in wetland environments. Beyond species detection, this project also delved into practical aspects of data transfer and storage efficiency, ensuring the system’s suitability for real-world applications. Modularity was another crucial consideration, simplifying maintenance and upgrades. Moreover, a preliminary cost analysis indicated the cost-effectiveness of the system compared to commercial alternatives. The integration of climate sensors into the monitoring system was explored as a future direction. This addition holds the potential to provide a more comprehensive approach to environmental monitoring by incorporating climate data into the analysis. Such a holistic approach can further enrich our understanding of bird behaviour in relation to changing environmental conditions. The findings of this research have significant implications for avian conservation and ecological studies, particularly in the unique context of South Africa. This project introduces an affordable and practical tool for monitoring bird species in wetland habitats, offering valuable insights into the preservation and management of these critical ecosystems.