Lovebird image classification based on convolutional neural network

Amelia Gizzela Sheehan Auni; Christy Atika Sari; Eko Hari Rachmawanto; Mohamed Doheir

doi:10.36055/tjst.v19i2.21946

Lovebird image classification based on convolutional neural network

Amelia Gizzela Sheehan Auni, Christy Atika Sari, Eko Hari Rachmawanto, Mohamed Doheir

Abstract

Lovebird is a type of bird from the Psittacidae family, consisting of 90 generations. One of them is the genus Agapornis Selby or Lovebird, which has 9 species. In recognizing the differences of each species, you can use the Object Recognition system. One of them uses the popular CNN algorithm. The dataset was obtained from open sources totaling 8,992 datasets from 9 Agapornis species. It consists of 80% training images and 20% testing images from several datasets. After 10 accuracy tests, the results stated that the accuracy rate reached 89%. In addition, there are also extraction features extracted from images including color, shape, size, and texture characteristics. The things extracted in this study include the Mean, Standard Deviation, Kurtosis, Skewness, Variance, Entropy Value, Maximum Pixel, and Minimum Pixel.

Keywords

Lovebird; agapornis; classification; CNN

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References

Farou, Z. (2022). Automatic Fine-grained Classification of Bird Species Using Deep Learning Eötvös Loránd University DEPT . of Data Science and Engineering Automatic Fine-grained Classification of Bird Species Using Deep Learning Supervisor : Zakarya Farou PhD student Auth,” No. December, 2022. Doi: 10.13140/RG.2.2.18648.57601.

Naushad Ali, M. M., Abdullah-Al-Wadud, M., & Lee, S. L. (2013). Moving object detection and tracking using particle filter. Appl. Mech. Mater., Vol. 321–324, No. 01, Pp. 1200–1204. Doi: 10.4028/www.scientific.net/AMM.321-324.1200.

Bellynza, K. A., & Syaputra, H. (2022). Objek Deteksi Burung Lovebird Menggunakan Instance Segmentation Mask R-Cnn. Bina Darma Conference on Computer Science (BDCCS), Vol. 4, No. 1, Pp. 245–254.

Sri, M. S., Naik, B. R., & Sankar, K. J. (2021). Object Detection Based on Faster R-Cnn. Int. J. Eng. Adv. Technol.. Vol. 10, No. 3, Pp. 72–76. Doi: 10.35940/ijeat.c2186.0210321.

van der Zwan, H., Visser, C., & van der Sluis, R. (2019). Plumage colour variations in the Agapornis genus: a review. Ostrich, Vol. 90, No. 1, Pp. 1–10. Doi: 10.2989/00306525.2018.1540446.

Peng, C., Zhang, C., Xue, X., Gao, J., Liang, H., & Niu, Z. (2022). Cross-modal complementary network with hierarchical fusion for multimodal sentiment classification. Tsinghua Sci. Technol., Vol. 27, No. 4, Pp. 664–679. Doi: 10.26599/TST.2021.9010055.

Zebari, R., Abdulazeez, A., Zeebaree, D., Zebari, D., & Saeed, J. (2020). A Comprehensive Review of Dimensionality Reduction Techniques for Feature Selection and Feature Extraction. J. Appl. Sci. Technol. Trends, Vol. 1, No. 2, Pp. 56–70. Doi: 10.38094/jastt1224.

Fung, K. L. Y. et al. (2020). Ultramicroscopy Accurate EELS background subtraction – an adaptable method in MATLAB. Ultramicroscopy, Vol. 217, No. June, P. 113052. Doi: 10.1016/j.ultramic.2020.113052.

Dharma, A. S., Tambunan, S., & Naibaho, P. K. (2022). Deteksi Objek Aksara Batak Toba Menggunakan Faster R-CNN dan YoloV3. [Online]. Available: https://www.academia.edu/68753590/Deteksi_Objek_Aksara_Batak_Toba_Menggunakan_Faster_R_CNN_dan_YoloV3

Irawan, F. A., Sudarma, M., & Care Khrisne, D. (2021). Rancang Bangun Aplikasi Identifikasi Penyakit Tanaman Pepaya California Berbasis Android Menggunakan Metode Cnn Model Arsitektur Squeezenet. J. SPEKTRUM, Vol. 8, No. 2, P. 18. Doi: 10.24843/spektrum.2021.v08.i02.p3.

Wang, X., Liu, Y., & Xin, H. (2021). Bond strength prediction of concrete-encased steel structures using hybrid machine learning method. Structures, Vol. 32, No. November 2020, Pp. 2279–2292. Doi: 10.1016/j.istruc.2021.04.018.

Andresini, G., Appice, A., & Malerba, D. (2021). Nearest cluster-based intrusion detection through convolutional neural networks. Knowledge-Based Syst., Vol. 216, P. 106798. Doi: 10.1016/j.knosys.2021.106798.

Charli, F., Syaputra, H., Akbar, M., Sauda, S., & Panjaitan, F. (2020). Implementasi Metode Faster Region Convolutional Neural Network (Faster R-CNN) Untuk Pengenalan Jenis Burung Lovebird. J. Inf. Technol. Ampera, Vol. 1, No. 3, Pp. 185–197. Doi: 10.51519/journalita.volume1.isssue3.year2020.page185-197.

Zaman, K., & Maghdid, S. S. (2021). Medical Images Classification for Skin Cancer Using Convolutional Neural Network Algorithms. Advances in Mechanics, Vol. 9, No. 3, Pp. 526-541.

Jia, W., Tian, Y., Luo, R., Zhang, Z., Lian, J., & Zheng, Y. (2020). Detection and segmentation of overlapped fruits based on optimized mask R-CNN application in apple harvesting robot. Comput. Electron. Agric., Vol. 172, No. March, p. 105380. Doi: 10.1016/j.compag.2020.105380.

Huang, R. et al. (2023). NLIP: Noise-Robust Language-Image Pre-training. Proc. 37th AAAI Conf. Artif. Intell. AAAI 2023, Vol. 37, Pp. 926–934.

Luo, T., Jiang, G., Yu, M., Zhong, C., Xu, H., & Pan, Z. (2019). Convolutional neural networks-based stereo image reversible data hiding method. J. Vis. Commun. Image Represent., Vol. 61, Pp. 61–73. Doi: 10.1016/j.jvcir.2019.03.017.

Bai, Q., Li, S., Yang, J., Song, Q., Li, Z., & Zhang, X. (2020). Object detection recognition and robot grasping based on machine learning: A survey. IEEE Access, Vol. 8, Pp. 181855–181879. Doi: 10.1109/ACCESS.2020.3028740.

Usha Kingsly Devi, K., & Gomathi, V. (2022). Deep Convolutional Neural Networks with Transfer Learning for Visual Sentiment Analysis. Neural Process. Lett., Vol. 55, No. 4, Pp. 5087–5120. Doi: 10.1007/s11063-022-11082-3.

Sabuj, H. H., et al. (2023). A Comparative Study of Machine Learning Classifiers for Speaker’s Accent Recognition. 2023 IEEE World AI IoT Congr. AIIoT 2023, Pp. 627–632. Doi: 10.1109/AIIoT58121.2023.10174511.

DOI: http://dx.doi.org/10.36055/tjst.v19i2.21946

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