Accuracy in Identifying Orchid Images Using Backpropagation Artificial Neural Network
Keywords: Accuracy, Identification, Orchid, Image Processing, Back Propagation
Orchids are ornamental flower plants in the Family Orchidaceae whose habitat is spread over almost all continents in the world, except Antarctica. There are so many orchid enthusiasts in Indonesia and this fact made orchids a promising commodity for ornamental plant cultivator. With a variety of orchid species that reach more than 25,000 species, the identification of orchid species becomes a little complicated for orchid lovers. The purpose of this study was to determine the accuracy level of orchid species identification through image recognition so that it can be used as a reference in determining the feasibility of this method. This study used 120 images of orchids in 6 species. The image of the orchid was obtained by shooting at several locations using the camera. The photo is then processed using image processing software by cropping and resizing to speed up computing time during network training. Furthermore, MatLab software is used to perform the feature extraction process in the form of color feature data and moment invariants. Data from feature extraction is used as input for training artificial neural networks using the Back Propagation method. Calculation of the level of accuracy done by testing the network using the test data that has been provided. The trial results show that 26 of 30 were successfully recognized so that the accuracy rate can be calculated, namely 86.7%. An accuracy rate of 86.7% can be considered feasible and can be used as a basis for consideration of using this tested method as the right method for identifying orchids through images.
Wikipedia (2020), Orchidaceae, (Updated 11 October 2020) available at https://en.wikipedia.org/wiki/Orchidaceae [Accessed 25 October 2020].
Chase, M., Christenhusz, M. & Mirenda, T (2017), The Book of Orchids. Brighton, UK: The Ivy Press Limited.
Teoh, E. S. (2019). Medicinal Orchid Usage in Rural Africa. Orchids as Aphrodisiac, Medicine or Food, 305–362. DOI: http://doi.org/10.1007/978-3-030-18255-7_17 .
BPS Indonesia (2020), Statistik Indonesia (Statistical Yearbook of Indonesia) 2019, Jakarta: Badan Pusat Statistik Indonesia.
Meisel, J. E., Kaufmann, R. S., & Pupulin, F. (2015). Orchids of Tropical America. New York, USA. Cornell University Press. DOI: http://doi.org/10.7591/9780801454929-005 .
Minarni, M., Salumbae, R., & Hasbi, Z. (2018). Implementasi Jaringan Syaraf Tiruan (JST) dan Pengolahan Citra Untuk Klasifikasi Kematangan TBS Kelapa Sawit. Komunikasi Fisika Indonesia, 15(1), 36. DOI: http://doi.org/10.31258/jkfi.15.1.36-45 .
Arwatchananukul, S., Kirimasthong, K., & Aunsri, N. (2020). A New Paphiopedilum Orchid Database and Its Recognition Using Convolutional Neural Network. Wireless Personal Communications, 155. DOI: http://doi.org/10.1007/s11277-020-07463-3 .
Devi, K. J., Devi, A. A., & Thongam, K. (2019). Automatic Speaker Recognition using MFCC and Artificial Neural Network. International Journal of Innovative Technology and Exploring Engineering, 9(1S), 39–42. DOI: http://doi.org/10.35940/ijitee.a1010.1191s19 .
Arasy, R., & Basari. (2019). Detection Of Hypertensive Retinopathy Using Principal Component Analysis (PCA) and Backpropagation Neural Network Methods. AIP Conference Proceedings 2092. DOI: http://doi.org/10.1063/1.5096735 .
Tuesta, V. A., Alcarazo, F. D., Mejia, H. I., & Forero, M. G. (2020). Automatic Classification of Citrus Aurantifolia Based on Digital Image Processing and Pattern Recognition. Applications of Digital Image Processing XLIII. DOI: http://doi.org/10.1117/12.2566888 .
Huixian, J. (2020). The Analysis of Plants Image Recognition Based on Deep Learning and Artificial Neural Network. IEEE Access, 8, 68828–68841. DOI: http://doi.org/10.1109/access.2020.2986946 .
Feng, X., He, P., Zhang, H., Yin, W., Qian, Y., Cao, P., & Hu, F. (2019). Rice Seeds Identification Based on Back Propagation Neural Network Model. International Journal of Agricultural and Biological Engineering, 12(6), 122–128. DOI: http://doi.org/10.25165/j.ijabe.20191206.5044 .
Moham, N., Dwiyanto, F. A., Pakpahan, H. S., Islamiyah, I., & Setyadi, H. J. (2019). Pengenalan Karakter Tulisan Menggunakan Metode Backpropagation Neural Network. Sains, Aplikasi, Komputasi dan Teknologi Informasi, 1(2), 14. DOI: http://doi.org/10.30872/jsakti.v1i2.2601 .
Gonzalez, R. C. & Woods, R. E. (2018). Digital Image Processing.4th. Ed. Harlow, Essex. Pearson Education Limited.
Sonka, M., Hlavac, V., Boyle, R. (2015). Image Processing, Analysis, and Machine Vision. 4th Ed. Stamford:CT. Chengage Learning.
Nixon, M. S., Aguado, A. S,. (2002). Feature Extraction and Image Processing, 1st Ed. Woburn, MA. Newnes.
How to Cite
Ovidius, A., Nurcahyo, G. W., Sumijan, & Salambue, R. (2021). Accuracy in Identifying Orchid Images Using Backpropagation Artificial Neural Network. Jurnal Informasi Dan Teknologi, 3(3), 95-102. https://doi.org/10.37034/jidt.v3i3.115
Copyright (c) 2021 Jurnal Informasi dan Teknologi
This work is licensed under a Creative Commons Attribution 4.0 International License.