Bird Species Classification System Using Transfer Learning
DOI:
Abstract
Manual recognition is limited by the observer's expertise and knowledge, which can lead to errors when observed by non-experts. The objective of this research is to create a machine learning algorithm that can accurately classify bird species based on physical features and develop a software system that includes the machine learning algorithm, allowing for efficient classification of different bird species. This research also wants to evaluate the accuracy of the approach in real-world scenarios. The methodology research uses the machine learning life cycle model and software development life cycle model. The research aims to provide a user-friendly interface that recommends bird species classifications based on uploaded images, ultimately contributing to a more accessible and informative bird identification experience. In this research, the F1-score with fine-tuning is reported as 0.8889. It is close to 1, it suggests a well-balanced performance in terms of correctly identifying positive instances which is precision, and capturing relevant positive instances which are recalled. Based on the result, the proposed system can enhance users' ability to accurately identify and classify various bird species through the utilization of a pre-trained convolutional neural network model.
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References
A. Clark and H. Johnson, "Audio Recognition Tools for Birdsong Identification: Current Technologies and Future Directions," Journal of Ornithology and Sound Analysis, vol. 34, no. 1, pp. 45-58, March 2024.
J. Smith and L. Brown, "Advancements in Automated Bird Species Identification: A Review," Journal of Avian Research, vol. 29, no. 3, pp. 123-136, June 2023.
K. Jones and M. Lee, "The Role of Citizen Science in Enhancing Bird Identification Accuracy," Ecological Monitoring Journal, vol. 15, no. 2, pp. 89-102, September 2023.
T. Brown and G. Evans, "Challenges and Solutions in Manual Bird Species Identification," Avian Science Review, vol. 26, no. 2, pp. 76-88, July 2024.
R. Patel and S. Nguyen, "Utilizing Drones for Improved Bird Monitoring and Species Identification," Remote Sensing and Conservation, vol. 22, no. 4, pp. 57-68, December 2024.
C. Smith and D. Wang, "Real-Time Bird Detection with YOLO in Aerial Imagery," Journal of Computer Vision and Pattern Recognition, vol. 44, no. 5, pp. 789-802, May 2024.
S. Roberts and J. Davis, "Image Recognition Technology for Bird Species Identification: Innovations and Applications," Journal of Ornithological Research, vol. 38, no. 2, pp. 155-168, May 2023.
R. Smith and K. Patel, "The Role of Image Recognition in Conservation Efforts: Case Studies and Future Directions," Journal of Conservation Biology, vol. 52, no. 1, pp. 123-136, August 2024.
Dharaniya, R., Kumar, P., & Kumar, M. "Advancements in Bird Species Recognition Using Deep Learning and UAV Imagery: Implications for Ecological Research and Conservation," ournal of Ecological Research, vol. 47, no. 3, pp. 211-225, April 2022.
A. Mushtaq, "Transfer Learning with Convolutional Neural Networks: Recent Advances and Applications," Journal of Computer Vision and Pattern Recognition, vol. 35, no. 7, pp. 1223-1238, July 2022.
B. Berno, L. Schneider, L. Albini, and H. Lopes, "Transfer Learning for Object Recognition and Bird Species Identification," Journal of Machine Learning Research, vol. 21, no. 3, pp. 101-115, March 2020.
J. Jiaogen, L. Zhang, X. Liu, and M. Wang, "Challenges in Manual Bird Species Classification: Issues of Similarity and Observer Expertise," International Journal of Ornithology Research, vol. 45, no. 6, pp. 789-804, June 2022.
X. Mao, J. Chow, P. Tan, K. Liu, J. Wu, Z. Su, and Y. Wang, "Challenges in Manual Bird Species Identification: The Role of Observer Expertise and Fine-Grained Features," Journal of Avian Research, vol. 39, no. 8, pp. 1024-1037, August 2021.
L. Gardner, V. Saccomanno, A. Steinkraus, A. Ortega, K. Brush, G. Yenni, and S. Ernest, "Transfer Learning for Bird Monitoring: Generalizing Across New Species, Ecosystems, and Image Resolutions," Journal of Computational Biology, vol. 28, no. 11, pp. 1547-1560, November 2021.
M. Alswaitti, L. Zihao, W. Alomoush, A. Alrosan, and K. Alissa, "Evaluating Transfer Learning Models for Bird Recognition Systems in Image Recognition," IEEE Access, vol. 10, pp. 15032-15045, January 2022.
A. Gupta, R. Sharma, S. Patel, and M. Singh, "Classifying Bird Species Using Convolutional and Recurrent Neural Networks: A Study on Vocalizations and Spectrogram Analysis," IEEE Transactions on Neural Networks and Learning Systems, vol. 32, no. 4, pp. 1025-1036, April 2021.
M. Deoniziak and T. Osiejuk, "The Impact of Noise Pollution on Bird Song Characteristics: An Ecological and Environmental Study," Journal of Avian Biology, vol. 52, no. 9, pp. 875-889, September 2021.
E. Morfi, D. L. G. Jansen, A. L. H. Tan, and P. M. L. G. Ferris, "Development of Richly Annotated Birdsongs Audio Datasets for Research on Bird Behavior and Communication," IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 12, no. 5, pp. 1447-1456, May 2019.
Y. Zhang, "Exploring Bird Species Classification with Citizen Science Data: Analyzing Spatial and Temporal Patterns in Volunteer Contributions," International Journal of Environmental Research and Public Health, vol. 17, no. 14, pp. 5030-5045, July 2020.
M. A. H. Haris and A. F. Saad, “Covid Classification System for Covid Detection,” Journal of Engineering, Technology, and Applied Science, vol. 6, no. 1, pp. 9-18, April 2024.
G. Aishwarya, V. Satyanarayana, M. Singh, and S. Kumar, "Artificial Intelligence: Algorithms and Models for Machine Intelligence," Journal of Computer Science and Technology, vol. 15, no. 4, pp. 789-805, December 2022.
M. Ramli, J. Ahmad, N. Ali, and R. Hasan, "Weak AI vs. Strong AI: Understanding the Spectrum of Artificial Intelligence and Its Future Implications," IEEE Access, vol. 11, pp. 10345-10358, March 2023.
N. Nadiah, M. F. Rahman, H. N. Sulaiman, and A. R. Ismail, "Application of AI in Public Health: Detecting Covid-19 Using Machine Learning Algorithms," International Journal of Health Informatics, vol. 26, no. 7, pp. 678-692, July 2022.
K. Kaushik, "Machine Learning Approaches for Disease Detection: A Case Study on Covid-19," Journal of Artificial Intelligence Research, vol. 58, no. 2, pp. 204-218, February 2023.
[3] A. K. Patel and B. Singh, "Machine Learning Techniques for Recommendation Systems: Recent Developments and Future Directions," IEEE Transactions on Computational Intelligence and AI in Games, vol. 14, no. 1, pp. 45-58, January 2023.
V. Kotu and B. Deshpande, Data Science, 2nd ed. Amsterdam, Netherlands: Elsevier, 2023.
"What is Computer Vision?," Microsoft Azure, 2023. [Online]. Available: https: //azure.microsoft.com/en-us/overview/ai-platform/computer-vision/. [Accessed: Jan. 05, 2024].
B. Marr, "The State of Computer Vision in 2021: Trends and Market Insights," Forbes, 19-Oct-2021. [Online]. Available: https: //www.forbes.com/sites/bernardmarr/2021/10/19/the-state-of-computer-vision-in-2021-trends-and-market-insights/. [Accessed: Jan. 05, 2024].
K. Yasar, "Image Classification: Techniques and Applications," Journal of Computer Vision and Artificial Intelligence, vol. 12, no. 3, pp. 67-80, June 2023.
I. Oguz, C. P. Perez, and H. A. Hwang, "Layered Optimal Graph Image Segmentation of Multiple Objects and Surfaces: Comparison with Manual Segmentation," IEEE Transactions on Medical Imaging, vol. 42, no. 9, pp. 2345-2356, September 2023.
A. Brown, "Understanding the Differences Between Image Classification and Image Segmentation," IEEE Transactions on Image Processing, vol. 32, no. 4, pp. 892-903, April 2023.
M. McMullen, "Comparing Image Classification and Image Segmentation Techniques in Computer Vision," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 46, no. 6, pp. 1345-1359, June 2024.
"What Is Deep Learning? - How It Works," NetApp, 2019. [Online]. Available: https: //www.netapp.com/data-vision/deep-learning/. [Accessed: Jan. 05, 2024].
J. R. Smith, "Deep Learning in Computer Vision: Recent Advances and Future Directions," IEEE Transactions on Computer Vision and Image Analysis, vol. 30, no. 2, pp. 215-229, February 2023.
M. A. Khan and S. R. Khan, "Deep Learning vs Traditional Computer Vision Techniques: A Comparative Study," IEEE Access, vol. 11, pp. 445-457, March 2024.
E. Egrioglu and A. Bas, "Transfer Learning for Computer Vision: A Survey," IEEE Transactions on Neural Networks and Learning Systems, vol. 34, no. 2, pp. 321-339, 2023.
R. Yamashita, M. Nishio, R. K. G. Do, and K. Togashi, "Convolutional Neural Networks: An Overview and Application in Radiology," Insights into Imaging, vol. 9, no. 4, pp. 611-629, 2018.
A. S. Khaidem and S. G. Deshmukh, "Recent Advances in Convolutional Neural Networks: Architectures and Applications," Journal of Machine Learning Research, vol. 25, no. 67, pp. 1-30, 2023.
A. R. Zahid, N. Ghaffar, N. Iqbal, and A. R. Ahmad, "Transfer Learning for Image Classification: A Comparative Study of Pre-trained Deep Learning Models," IEEE Access, vol. 10, pp. 200-211, January 2022.
Kumar, A., & Singh, A., "A Comprehensive Review of Machine Learning Life Cycle and Its Applications," IEEE Access, vol. 11, pp. 50530-50545, March 2023.
Shafiq, M., Hanif, M. A., Sattar, N., & Rehman, M. A., "Machine Learning Life Cycle: From Data Gathering to Model Deployment," Journal of Computer Science and Technology, vol. 36, no. 4, pp. 798-812, August 2021.
