Assessing Biodiversity And Taxonomic Diversity Through Environmental DNA Datasets Using Deep Learning

Accurate biodiversity assessment is vital for effective ecosystem monitoring and conservation. Traditional methods are often invasive, labor-intensive, and limited in scope. Environmental DNA (eDNA) analysis provides a non-invasive approach to detect species from environmental samples such as water, soil, and air. In this study, machine learning techniques are applied to eDNA da-tasets to classify taxa, predict species richness, and enhance detection of rare or cryptic species. Features such as sequence patterns and taxonomic markers are extracted from high-throughput sequencing data. Models including support vector machines, random forests, and deep learning are evaluated using standard performance metrics. The results demonstrate that machine learning-driven eDNA analysis enables efficient, scalable, and accurate biodiversity assessment, support-ing data-driven conservation strategies.