Bird Species Recognition System with Fine-Tuned Model

Ching-Yang Ngo, Lee-Ying Chong, Siew-Chin Chong, Pey-Yun Goh

Abstract


A bird recognition system identifies bird species by combining computer vision and machine learning techniques to categorize different bird species with high accuracy. Moreover, the bird species recognition system represents a significant advance in animal protection and zoological research, especially for the rare and elusive bird species living in the jungle. This work focuses on an image-based system for bird species recognition. In bird species recognition, users input the bird images, and the system uses a deep learning model trained for optimal results in identifying different bird species from the images. We used fine-tuned deep learning models (Inception-V3 and EfficientNet-B4) to evaluate and determine which model can best perform image-based bird species recognition. Several unique datasets were used to evaluate and determine which model was best suited for image-based bird species recognition. These datasets consist of CUB -200-2011, Kaggle-510 bird species, 325 bird species, and a self-generated dataset (100 bird species from Malaysia). When applied to these four different datasets, the experimental results clearly show the advantage of fine-tuning the deep learning models. This study makes an important contribution to ornithology by providing a robust and trustworthy method for identifying and cataloging bird species, especially those that are rarely seen in the wild. Thus, the bird identification system is important for scientific research and animal welfare.

Keywords


Bird species recognition system; fine-tuned model; machine learning; deep learning

Full Text:

PDF

References


C. Collins, D. Dennehy, K. Conboy, P. Mikalef, “Artificial intelligence in information systems research: A systematic literature review and research agenda,†International Journal of Information Management, vol. 60, 2021, doi: 10.1016/j.ijinfomgt.2021.102383.

MC. Tai MC, “The impact of artificial intelligence on human society and bioethics,†Tzu Chi Medical Journal, vol. 32, 2020, pp. 339-343, doi: 10.4103/tcmj.tcmj_71_20.

P. Bhatt and A. Muduli, “Artificial intelligence in learning and development: a systematic literature review,†European Journal of Training and Development, vol. 47, 2023, pp. 677-694,

doi: 10.1108/EJTD-09-2021-0143.

Y. M. Rosas, P. L. Peri, J. Benítez, M. V. Lencinas, N. Politi, and G. M. Pastur, “Potential biodiversity map of bird species (Passeriformes): Analyses of ecological niche, environmental characterization and identification of priority conservation areas in southern Patagonia,†Journal for Nature Conservation, vol. 73, 126413, 2023,

doi: 10.1016/j.jnc.2023.126413.

L. Cardador and T. M. Blackburn, “A global assessment of human influence on niche shifts and risk predictions of bird invasions,†Global Ecology and Biogeography, vol 29, no. 11, 2020, pp. 1956–1966, doi: 10.1111/geb.13166.

B. Naimi, C. Capinha, J. Ribeiro, C. Rahbek, D. Strubbe, L. Reino, M. B. Araújo, "Potential for invasion of traded birds under climate and land-cover change," Global Change Biology, pp. 1-13, 2022,

doi: 10.1111/gcb.16310.

C. H. Oliveros, D. J. Field, D. T. Ksepka, et al., “Earth history and the passerine superradiation,†in Proceedings of the National Academy of Sciences of the United States of America, vol 116, no. 16, pp. 7916-7925, 2019, doi: 10.1073/pnas.1813206116.

A. E. Barnes, J. G. Davies, B. Martay, P. H. Boersch-Supan, S. J. Harris, D. G. Noble, J. W. Pearce-Higgins and R. A. Robinson, “Rare and declining bird species benefit most from designating protected areas for conservation in the UK,†Natural Ecology & Evolution, vol. 7, pp. 92–101, 2023, doi: 10.1038/s41559-022-01927-4.

X. Yang, S. Li, A. Hughes and G. Feng, “Threatened bird species are concentrated in regions with less historical human impacts,†Biological Conservation, vol. 255, 2021,

doi: 10.1016/j.biocon.2021.108978.

M. M. Taye, "Theoretical Understanding of Convolutional Neural Network: Concepts, Architectures, Applications, Future Directions," Computation, vol 11, no. 3:52, 2023,

doi: 10.3390/computation11030052.

J. Florentin, T. Dutoit, and O. Verlinden, “Detection and identification of European woodpeckers with deep convolutional neural networks,†Ecological Informatics, vol. 55, p.101023, 2020,

doi: 10.1016/j.ecoinf.2019.101023.

A. Khot, "Image analysis using convolutional neural network to detect bird species," 7th International Conference on Computing in Engineering & Technology (ICCET 2022), Online Conference, 2022, pp. 58-61, doi: 10.1049/icp.2022.0592.

L. Alzubaidi, J. Zhang, A.J. Humaidi, et al., “Review of deep learning: concepts, CNN architectures, challenges, applications, future directions,†Journal of Big Data, vol 8, no. 53, 2021,

doi: 10.1186/s40537-021-00444-8.

I. H. Sarker, “Deep Learning: A Comprehensive Overview on Techniques, Taxonomy, Applications and Research Directions,†SN Computer Science, vol. 2, p. 420, 2021, doi: 10.1007/s42979-021-00815-1.

B. Qiao, Z. Zhou, H. Yang and J. Cao, "Bird species recognition based on SVM classifier and decision tree," in 2017 First International Conference on Electronics Instrumentation & Information Systems (EIIS), Harbin, China, 2017, pp. 1–4,

doi: 10.1109/EIIS.2017.8298548.

P. Gavali and J. S. Banu, "Bird species identification using deep learning on GPU platform," in 2020 International Conference on Emerging Trends in Information Technology and Engineering (ic-ETITE), Vellore, India, 2020, pp. 1–6, doi: 10.1109/ic-ETITE47903.2020.85.

N. Jain, M. Kamble, A. Kanojiya and C. Jage, “Implementation of bird species detection algorithm using deep learning,†in ITM Web Conference, vol. 44, 2022, pp. 1–6, Art. no. 03042,

doi: 10.1051/itmconf/20224403042.

N. Sharma, A. Vijayeendra, V. Gopakumar, P. Patni and A. Bhat, "Automatic identification of bird species using audio/video processing," in 2022 International Conference for Advancement in Technology (ICONAT), 2022, pp. 1–6,

doi: 10.1109/ICONAT53423.2022.9725906.

H. Wang, Y. Xu, Y. Yu, Y. Lin, and J. Ran, “An efficient model for a vast number of bird species identification based on acoustic features,†Animals, vol. 12, no. 18, p. 2434, 2022, doi: 10.3390/ani12182434.

R. Pahuja and A. Kumar, “Sound-spectrogram based automatic bird species recognition using MLP classifier,†Applied Acoustics, vol. 180, 2021, Art. no. 108077, doi: 10.1016/j.apacoust.2021.108077.

K. M. Ragib, R. T. Shithi, S. A. Haq, M. Hasan, K. M. Sakib and T. Farah, “PakhiChini: Automatic bird species identification using deep learning,†in 2020 Fourth World Conference on Smart Trends in Systems, Security and Sustainability (WorldS4), London, UK, 2020, pp. 1–6, doi: 10.1109/WorldS450073.2020.9210259.

M. Alswaitti, L. Zihao, W. Alomoush, A. Alrosan and K. Alissa, “Effective classification of birds’ species based on transfer learning,†International Journal of Electrical and Computer Engineering (IJECE), no. 4, vol. 12, 2022, pp. 4172–4184,

doi: 10.11591/ijece.v12i4.pp4172-4184.

D. Jha, and M. Sundaram, “Bird species identification using deep learning and image processing,†EasyChair, 2022, Preprint no. 8319.

J. Gómez-Gómez, E. Vidaña-Vila and X. Sevillano, “Western Mediterranean Wetland Birds dataset: A new annotated dataset for acoustic bird species classificationâ€, Ecological Informatics, vol. 75, 2023, Art. no. 102014, doi: 10.1016/j.ecoinf.2023.102014.

H. K. Kondaveeti, P. Nithiyasri, B. S. L. Sri, K. H. Jessica, S. V. S. Kumar and S. C. Gopi, "Bird Species Recognition using Deep Learning," 2023 3rd International conference on Artificial Intelligence and Signal Processing (AISP), VIJAYAWADA, India, 2023, pp. 1-6, doi: 10.1109/AISP57993.2023.10134804.

S. V. S. Kumar and H. K. Kondaveerti, "A Comparative Study on Deep Learning Techniques for Bird Species Recognition," 2023 3rd International Conference on Intelligent Communication and Computational Techniques (ICCT), Jaipur, India, 2023, pp. 1-6, doi: 10.1109/ICCT56969.2023.10075901.

Y. -P. Huang and H. Basanta, "Bird Image Retrieval and Recognition Using a Deep Learning Platform," in IEEE Access, vol. 7, pp. 66980-66989, 2019, doi: 10.1109/ACCESS.2019.2918274.

MY, Xin, L. W. Ang, and S. Palaniappan, “A Multi-Scale Feature Attention Image Recognition Algorithm,†Journal of Informatics and Web Engineering (JIWE), vol. 2, no. 2, pp. 1-7, 2023,

doi: 10.33093/jiwe.2023.2.2.1.

C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna, “Rethinking the Inception Architecture for Computer Vision,†Computer Vision and Pattern Recognition, 2015, pp. 2818-2826,

doi: 10.48550/arXiv.1512.00567.

A. F. Agarap, “Deep Learning using Rectified Linear Units (ReLU),†Neural and Evolutionary Computing (cs.NE), 2019,

doi: 10.48550/arXiv.1803.08375.

M. Wang, S. Lu, D. Zhu, J. Lin and Z. Wang, "A High-Speed and Low-Complexity Architecture for Softmax Function in Deep Learning," 2018 IEEE Asia Pacific Conference on Circuits and Systems (APCCAS), Chengdu, China, 2018, pp. 223-226,

doi: 10.1109/APCCAS.2018.8605654.

M. Tan, and Q. V. Le, "EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks, " in Proceedings of the 36th International Conference on Machine Learning, Long Beach, CA, USA, 2019, pp. 6105-6114, doi: 10.48550/arXiv.1905.11946.

C-C. Wong, L-Y. Chong, S-C. Chong, and C-Y. Law, “QR Food Ordering System with Data Analyticsâ€, Journal of Informatics and Web Engineering (JIWE), vol. 2, no. 2, pp. 249–272, 2023,

doi: 10.33093/jiwe.2023.2.2.18.




DOI: http://dx.doi.org/10.18517/ijaseit.13.5.19030

Refbacks

  • There are currently no refbacks.


Published by INSIGHT - Indonesian Society for Knowledge and Human Development