Deep Learning Approaches for Monitoring and Preserving Ecological Biodiversity: Challenges and Innovations

Autores/as

  • ShabanaFathima M Assistant Professor, Department of Computer Science and Business Systems, Ramco Institute of Technology, Rajapalayam Autor/a
  • Shanmugapriya I Assistant Professor, Department of Computer Science,PSG College of Arts and Science, Coimbatore Autor/a
  • Pavithira L Assistant Professor, Department of Computer Science,PSG College of Arts and Science, Coimbatore Autor/a
  • Mihirkumar B. Suthar Associate Professor,Zoology Department,K.K.Shah Jarodwala Maninagar Science College, Ahmedabad Autor/a
  • Rajeswari J Assistant professor,Electronics and communication Engineering,Agni College of Technology, Chennai. Autor/a
  • Revathi R Assistant Professor, Department of CS-IT, PSGR Krishnammal College for Women, Coimbatore. Autor/a
  • Vengatesh T Assistant Professor, Department of CS-IT, PSGR Krishnammal College for Women, Coimbatore. Autor/a

DOI:

https://doi.org/10.48047/qq823b94

Palabras clave:

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.

Resumen

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.

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Referencias

Bonney, R., Phillips, T. B., Ballard, H. L., &Enck, J. W. (2021). Citizen science:Involving the public in biodiversity monitoring. Science, 372(6542), 978-982. 2. Carranza-Rojas, J., Goeau, H., Bonnet, P., Mata-Montero, E., & Joly, A. (2017). Going deeper in the automated identification of herbarium specimens. BMC Evolutionary Biology, 17(1), 1-14.

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Publicado

2025-02-03

Número

Vol. 54 Núm. 3 (2025): Year 2025, Volume 54, Issue 3

Sección

Articles

Licencia

Creative Commons License

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Cómo citar

Deep Learning Approaches for Monitoring and Preserving Ecological Biodiversity: Challenges and Innovations (S. . M, S. I, P. L, M. . B. Suthar, R. J, R. . R, & V. T , Trans.). (2025). Cuestiones De Fisioterapia, 54(3), 844-858. https://doi.org/10.48047/qq823b94