Citation

Ngo, Ching Yang and Chong, Lee Ying and Chong, Siew Chin and Goh, Pey Yun (2023) Bird Species Recognition System with Fine-Tuned Model. International Journal on Advanced Science, Engineering and Information Technology (IJASEIT), 13 (5). pp. 1719-1726. ISSN 2088-5334

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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.

Item Type:	Article
Uncontrolled Keywords:	Machine learning, deep learning
Subjects:	Q Science > Q Science (General) > Q300-390 Cybernetics
Divisions:	Faculty of Information Science and Technology (FIST)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	01 Dec 2023 01:20
Last Modified:	01 Dec 2023 01:20
URII:	http://shdl.mmu.edu.my/id/eprint/11888

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