A Data-Driven Approach Using Deep Learning for the Classification of Indian Bird Species Facing Climate Change Challenges: Implications for Biodiversity Conservation

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Abstract

Climate change poses a significant threat to global biodiversity, particularly impacting bird populations, which face the risk of extinction due to habitat loss and altered environmental conditions. This research addresses the difficulties of identifying and monitoring bird species in biodiversity-rich countries such as India. We propose an innovative approach that employs deep learning techniques to classify Indian bird species impacted by climate change. The study's primary aim is to create a precise and dependable classification algorithm using the advanced Inception-ResNet-v2 architecture, enhanced through transfer learning methods. To improve model reliability, we introduce a novel data augmentation technique that minimizes similarities between different species while refining fine-grained features. Our validation strategy includes a comprehensive approach with data swapping between training and validation sets, alongside fivefold cross-validation, ensuring strong predictive performance. The proposed methodology is evaluated on a diverse range of datasets, encompassing images of Indian bird species captured in various habitats and under differing environmental conditions. Initial findings reveal impressive classification accuracy, averaging 94%, with a precision of 96% across more than eight species categories. These outcomes highlight the model's potential for widespread application in biodiversity monitoring and conservation efforts. Future directions for this research include expanding the dataset, enhancing augmentation techniques, and investigating real-time monitoring capabilities. Through this work, we aim to make a meaningful contribution to conservation strategies for Indian bird species amidst the challenges of climate change.

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