Mobile Application for Breeding Bird Classification using Deep Learning Technique

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Phummiphak Promrangka
Saharat Wanthong
Choopan Rattanapoka


Currently, several bird parks have open aviaries where visitors can see various bird species up close. However, visitors are occasionally unable to identify the bird species they are watching. Because the bird species found in open aviaries are not typically seen in everyday life, and there are no zoo signs inside the open aviaries. Therefore, this article proposes the design and development of a mobile application using the Flutter framework. The application can detect and classify 10 bird species using a deep learning model named EfficientDet Lite, which is created by the TFLite model maker library. The users can use a smartphone camera to examine the birds. Then, when birds are found, the application will provide users the bird information. From the experiments, we found that the EfficientDet Lite 0 gave the most suitable results for the application. The model took 62.3 ms for the inference time with the precision, recall, accuracy and F1-score of 0.94, 0.94, 0.99, and 0.94, respectively.


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How to Cite
Promrangka ภ., Wanthong ส., and Rattanapoka ช., “Mobile Application for Breeding Bird Classification using Deep Learning Technique”, JIST, vol. 12, no. 1, pp. 37–46, Jun. 2022.
Research Article: Soft Computing (Detail in Scope of Journal)


J. Law, "Why we need birds (far more than they need us)", BirdLife International, 2021. [Online]. Available: [Accessed 15 November 2021].

P. Chatayapha. “Technology and early childhood in 21st century,” Journal of Graduate Studies Valaya Alongkorn Rajabhat University, vol. 14, no. 3, 2020.

"Cross-platform mobile frameworks used by global developers 2021 | Statista", Statista, 2021. [Online]. Available: worldwide-software-developer-workinghours/?fbclid=IwAR1 QrmP_JDP_yMdGu_C56ami-xPuUniTqCv-D0W1Eyw8cGJ NrVK-7ZmfAaU. [Accessed 15 November 2021].

S. B. Kotsiantis, I. Zaharakis, and P. Pintelas, “Supervised machine learning: A review of classification techniques,” Emerging Artificial Intelligence Applications in Computer Engineering, vol. 160, no. 1, pp. 3-24, 2007.

M. Tabak, et al, “Machine learning to classify animal species in camera trap images: Applications in ecology,” Methods in Ecology and Evolution, vol. 10, no. 4, pp. 585-590, 2018.

R. L. Galvez, A. A. Bandala, E. P. Dadios, R. R. P. Vicerra and J. M. Z. Maningo, “Object detection using convolutional neural networks,” TENCON 2018 - 2018 IEEE Region 10 Conference, pp. 2023-2027, 2018.

“Flutter - Beautiful native apps in record Time”,, 2021. [Online]. Available: [Accessed 15 November 2021].

“TensorFlow”, TensorFlow, 2015. [Online]. Available: [Accessed 15 November 2021].

“TensorFlowLite”, TensorFlow, 2018. [Online]. Available: [Accessed 15 November 2021].

S. Albawi, T. A. Mohammed and S. Al-Zawi, “Understanding of a convolutional neural network”. In 2017 International Conference on Engineering and Technology (ICET), pp. 1-6, 2017.

M. Tan, R. Pang and Q. V. Le, "EfficientDet: scalable and efficient object detection", Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp.1-10, 2020.

M. Tan and Q. V. Le, "EfficientNet: rethinking model scaling for convolutional neural networks", International Conference on Machine Learning, pp.1-11, 2019.

"TensorFlow Lite Model Maker", TensorFlow, 2020. [Online]. Available: model_maker. [Accessed 15 November 2021].

"Object Detection with TensorFlow Lite Model Maker", TensorFlow, 2020. [Online]. Available: lite/tutorials/model_maker_object_detection. [Accessed 15 November 2021].

M. X. He and P. Hao, "Robust Automatic Recognition of Chinese License Plates in Natural Scenes," in IEEE Access, vol. 8, pp. 173804-173814, 2020.

"The Zoological Park Organization of Thailand",, 2021. [Online]. Available: [Accessed 15 November 2021].