Pavithira, L (2025) Deep Learning Approaches for Monitoring and Preserving Ecological Biodiversity: Challenges and Innovations. Deep Learning Approaches for Monitoring and Preserving Ecological Biodiversity: Challenges and Innovations, 54 (3). pp. 844-858.

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Abstract

Ecological biodiversity is essential for maintaining ecosystem balance, supporting food security,
and promoting sustainable development. However, biodiversity faces significant threats due to
habitat loss, climate change, pollution, and human activities. Traditional monitoring techniques
often struggle to provide real-time, scalable, and accurate assessments of biodiversity. Deep
learning, a subset of artificial intelligence, has emerged as a powerful tool for biodiversity
monitoring and conservation. By leveraging convolutional neural networks (CNNs), recurrent
neural networks (RNNs), and transformer models, deep learning enables automated species
identification, habitat assessment, and environmental monitoring through image, acoustic, and
remote sensing data.Despite its transformative potential, deep learning in biodiversity conservation
presents challenges, including data scarcity, model interpretability, computational costs, and
ethical concerns. Innovations such as self-supervised learning, federated learning, and edge AI are
paving the way for more efficient and scalable conservation efforts. This paper explores the state�of-the-art deep learning approaches for biodiversity monitoring, highlights key challenges, and
discusses emerging innovations to enhance ecological preservation. Through a systematic analysis,
the study provides insights into the integration of AI-driven solutions in biodiversity conservation,
aiming to bridge technological advancements with sustainable environmental practices.

Item Type: Article
Uncontrolled Keywords: Ecological Biodiversity, Deep Learning, Biodiversity Monitoring, Artificial Intelligence, Conservation Technology, CNNs, RNNs, Transformer Models, Remote Sensing, Bioacoustic Monitoring, Species Identification, Habitat Assessment, Machine Learning in Ecology
Divisions: PSG College of Arts and Science > Department of Biotechnology
Depositing User: Dr. B Sivakumar
Date Deposited: 10 Apr 2026 07:22
Last Modified: 10 Apr 2026 07:22
URI: https://ir.psgcas.ac.in/id/eprint/2791

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