New Species Orchid Recognition System Using Convolutional Neural Network

  • Annisa Atikah Mohd Fadzil
  • Itaza Afiani Mohtar UiTM Perak Branch Tapah Campus


Orchid is famous for its variety and its beauty. Every year, about a hundred of new species names are published. The study is to determine whether the orchid species can be recognized using a convolutional neural network algorithm and to test the accuracy of the classification model. Transfer learning was also implemented in this project to skip the feature extraction phase that requires many computational resources in the CNN algorithm. The model of transfer learning that is used is the Inception V3 model. This project is to prove that the concept of new orchid species recognition can be done. The web application that created using HTML and Flask was able to recognize new species based on existing species. In this project, 10 existing species with 100 images each was selected in training, validating, and testing phase. The training accuracy reached 97% and the functional testing of orchid recognition results shows 83% accuracy with 1000 datasets. In conclusion, the use of a web system as a prototype tool for the recognition of new orchid species is helpful for the unlicensed persons/organization.


[1] A. Segovia-Rivas, J. A. Meave, E. J. González, and E. A. Pérez-Garc’ia, “Experimental
reintroduction and host preference of the microendemic and endangered orchid Barkeria
whartoniana in a Mexican Tropical Dry Forest,” J. Nat. Conserv., vol. 43, pp. 156–164, 2018.
[2] C.L Brown, “Can legislation deliver conservation? An assessment of the Threatened Species
Conservation Act 1995 (NSW) using two threatened plant species as case studies.” PhD
Thesis, Wollongong University, 2002.
[3] M. M. Hossain , R. Kant , P. T. Van , B. Winarto , S. Zeng , J. A. Teixeira da Silva “The
application of biotechnology to orchids” Critical Reviews in Plant Sciences, 32(2), pp 69-139.
[4] Hinsley A, de Boer HJ, Fay MF, Gale SW, Gardiner LM, Gunasekara RS, Kumar P, Masters
S, Metusala D, Roberts DL, Veldman S, Wong S, Phelps J. “A review of the trade in orchids,
and its implications for conservation.” Bot J Linn Soc 186, pp 435–455. 2018 .
[5] Mark W. Chase, Kenneth M. Cameron, John V. Freudenstein, Alec M. Pridgeon, Gerardo
Salazar, Cássio van den Berg, André Schuiteman, An updated . classification of Orchidaceae,
Botanical Journal of the Linnean Society, vol 177, Issue 2, pp 151–174, 2015
[6] Britannica, T. Editors of Encyclopaedia, “Species Plantarum. Work by Linnaues”,
Encyclopedia Britannica, 8th ed, Oxford UP, 2009.
[7] D. Inayah, T. Artika and E. Arisoesilangsih. “Mapping diverseterestrial orchidhs grown in the
orchids garden of the ranu darungan resort, the bromo tengger semeru national park” IOP
Conference Series: Earth and Environmental Science, vol 743, The 11th International
Conference on Global Resource Conservation 28-29 July 2020, East Java, Indonesia. 2020
[8] I. Goodfellow, Y. Bengio and A. Courville. “Deep Learning.” MIT Press, 2016.
[9] R., Poojary, and A. Pai “Comparative Study of Model Optimization Techniques in Fine-Tuned
CNN Models.” In 2019 International Conference on Electrical and Computing Technologies
and Applications (ICECTA) pp. 1-4. 2019.
[10] N., Mpofu and M., Sears, “Binary View Classification of Echocardiograms of the Heart Using
Transfer Learning.” In 2020 IEEE 4th International Conference on Image Processing,
Applications and Systems (IPAS) pp. 46-52, 2020.
How to Cite
MOHD FADZIL, Annisa Atikah; MOHTAR, Itaza Afiani. New Species Orchid Recognition System Using Convolutional Neural Network. Mathematical Sciences and Informatics Journal, [S.l.], p. 35-43, nov. 2021. ISSN 2735-0703. Available at: <>. Date accessed: 25 sep. 2023. doi:

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