AI-Based Biodiversity Monitoring for Conservation Efforts
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Abstract
The loss of biodiversity due to habitat destruction and climate change necessitates efficient conservation strategies. This paper investigates machine learning applications in biodiversity monitoring, including AI-powered species identification, habitat assessment, and ecological trend analysis. Techniques such as deep learning-based image recognition, acoustic monitoring, and geospatial analysis are employed to track endangered species and assess ecosystem health. Case studies highlight AI-driven conservation projects that improve wildlife protection and contribute to global biodiversity preservation.
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