Integrating Health Informatics and Deep Learning to Evaluate Climate Change Effects on Indian Bird Health

Abstract

Climate change represents a significant threat to global biodiversity, particularly affecting avian species due to their sensitivity to environmental changes impacting habitats, breeding cycles, and overall health. This study focuses on assessing the health of bird species in India as they respond to climate-related challenges, employing advanced image analysis techniques. Using visual data, we examine physical and behavioral changes in birds that may signal health issues, such as malnutrition, feather discoloration, and decreasing population density. We developed and trained a deep convolution neural network (DCNN) to classify health indicators across a range of species. Initial findings demonstrate a strong ability to identify health markers unique to each species, indicating the model’s potential role in supporting biodiversity conservation. By concentrating on India’s distinct avian population, this research underscores the value of technological methods in helping address climate change impacts on vulnerable wildlife species.