Birdwatcher’s Assistant: AI- Based Bird Species Classification


Year : 2025 | Volume : 12 | Issue : 01 | Page : 17-27
    By

    Salman Khursheed Ahmad,

  • Shreya Sachan,

  • Tanay Gupta,

  • Sakshi Rai,

  1. Assistant Professor, Department of Computer Science & Engineering, Galgotias University, Greater Noida, Uttar Pradesh, India
  2. Student, B. Tech Student, Department of Computer Science & Engineering-Artificial Intelligence, G L Bajaj Institute of Technology & Management, Greater Noida, Uttar Pradesh, India
  3. Student, B. Tech Student, Department of Computer Science & Engineering-Artificial Intelligence, G L Bajaj Institute of Technology & Management, Greater Noida, Uttar Pradesh, India
  4. Student, B. Tech Student, Department of Computer Science & Engineering-Artificial Intelligence, G L Bajaj Institute of Technology & Management, Greater Noida, Uttar Pradesh, India

Abstract

Many bird species have gone extinct because of human activities and changes in the climate. The loss of habitats is a significant danger to global biodiversity. Therefore, it is important to monitor species distribution and identify the components of biodiversity in an area to develop conservation strategies. The ultimate objective is to build a machine learning model that can accurately differentiate between various bird species based solely on visual cues. The core of this project is a rigorous process involving complete data processing, robust model architecture development, rigorous training, and thorough evaluation. The ultimate desire is to engineer a stable classification system that can identify a broad spectrum of bird species. Distinguishing between different types of birds is no easy feat. It can be quite perplexing, resulting in uncertain categorization and even sparking debates among bird experts and enthusiasts, as well as anteaters, on the exact species being observed. This difficulty presents a formidable test for both human and artificial vision. Despite the presence of shared characteristics among various bird species, their shapes and appearances can differ significantly. In addition to identifying key species, the project provides a comprehensive portrait of each bird theme, delving into important ecological nuances such as suitable habitat and food preferences. These fundamental insights help us gain a deeper understanding of these incredible creatures and provide invaluable knowledge for conservation efforts.

Keywords: Bird species identification, machine learning model, preferred habitats, conservation knowledge, avian biodiversity.

[This article belongs to Journal of Artificial Intelligence Research & Advances ]

How to cite this article:
Salman Khursheed Ahmad, Shreya Sachan, Tanay Gupta, Sakshi Rai. Birdwatcher’s Assistant: AI- Based Bird Species Classification. Journal of Artificial Intelligence Research & Advances. 2024; 12(01):17-27.
How to cite this URL:
Salman Khursheed Ahmad, Shreya Sachan, Tanay Gupta, Sakshi Rai. Birdwatcher’s Assistant: AI- Based Bird Species Classification. Journal of Artificial Intelligence Research & Advances. 2024; 12(01):17-27. Available from: https://journals.stmjournals.com/joaira/article=2024/view=191597


References

  1. Kwan C, Mei G, Zhao X, Ren Z, Xu R, Stanford V, Rochet C, Aube J, Ho KC. Bird classification algorithms: theory and experimental results. Proc IEEE Int Conf Acoust Speech Signal Process. 2004 May; 5: V-289.
  2. Burghardt T, Thomas B, Barham PJ, Calic J. Automated visual recognition of individual African penguins. Proc Int Penguin Conf. 2004 Sep; 7: 1–5.
  3. Lin H, Lin H, Chen W. Study on recognition of bird species in Minjiang River Estuary Wetland. Procedia Environ Sci. 2011; 10: 2478–83.
  4. Juliet S. Image-based bird species identification using machine learning. Proc IEEE Int Conf Adv Comput Commun Syst. 2023 Mar; 1: 1963–8.
  5. Lopes MT, Gioppo LL, Higushi TT, Kaestner CA, Silla CN Jr, Koerich AL. Automatic bird species identification for large number of species. Proc IEEE Int Symp Multimedia. 2011 Dec; 117–22.
  6. Marini A, Turatti AJ, Britto AS, Koerich AL. Visual and acoustic identification of bird species. Proc IEEE Int Conf Acoust Speech Signal Process. 2015 Apr; 2309–13.
  7. Atanbori J, Duan W, Murray J, Appiah K, Dickinson P. Automatic classification of flying bird species using computer vision techniques. Pattern Recognit Lett. 2016 Oct; 81: 53–62.
  8. Rai P, Golchha V, Srivastava A, Vyas G, Mishra S. An automatic classification of bird species using audio feature extraction and support vector machines. Proc IEEE Int Conf Invent Comput Technol. 2016 Aug; 1: 1–5.
  9. Roslan R, Nazery NA, Jamil N, Hamzah R. Color-based bird image classification using support vector machine. Proc IEEE Global Conf Consumer Electron. 2017 Oct; 1–5.
  10. Kumar A, Das SD. Bird species classification using transfer learning with multistage training. Proc Comput Vision Appl Worksh. 2019 Dec; 28–38.
  11. Hassanat AB. Furthest-pair-based binary search tree for speeding big data classification using k-nearest neighbors. Big Data. 2018 Sep; 6(3): 225–35.
  12. Yadav SP, Jindal M, Rani P, de Albuquerque VH, dos Santos Nascimento C, Kumar M. An improved deep learning-based optimal object detection system from images. Multimed Tools Appl. 2024 Mar; 83(10): 30045–72.
  13. Kaur J, Saxena J, Shah J, Yadav SP. Facial emotion recognition. Proc IEEE Int Conf Comput Intell Sustain Eng Solut. 2022 May; 528–33.
  14. Goel D, Singh D, Gupta A, Yadav SP, Sharma M. An efficient approach to predict the quality of apple through its appearance. Proc IEEE Int Conf Comput Electron Electr Eng Appl. 2023 Jun; 1–6.
  15. Acevedo MA, Corrada-Bravo CJ, Corrada-Bravo H, Villanueva-Rivera LJ, Aide TM. Automated classification of bird and amphibian calls using machine learning: a comparison of methods. Ecol Inform. 2009 Sep; 4(4): 206–14.
  16. Bardeli R, Wolff D, Kurth F, Koch M, Tauchert KH, Frommolt KH. Detecting bird sounds in a complex acoustic environment and application to bioacoustic monitoring. Pattern Recognit Lett. 2010 Sep; 31(12): 1524–34.
  17. Beckers GJ. Bird speech perception and vocal production: a comparison with humans. Hum Biol. 2011 Apr; 83(2): 191–212.
  18. Bermúdez-Cuamatzin E, Ríos-Chelén AA, Gil D, Garcia CM. Experimental evidence for real-time song frequency shift in response to urban noise in a passerine bird. Biol Lett. 2011 Feb; 7(1): 36–8.
  19. Bolhuis JJ, Okanoya K, Scharff C. Twitter evolution: converging mechanisms in birdsong and human speech. Nat Rev Neurosci. 2010 Nov; 11(11): 747–59.
  20. Brandes TS. Automated sound recording and analysis techniques for bird surveys and conservation. Bird Conserv Int. 2008 Sep; 18(S1): S163–73.
Regular Issue Subscription Review Article
Volume 12
Issue 01
Received 24/05/2024
Accepted 24/12/2024
Published 30/12/2024
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