New Species Orchid Recognition System Using Convolutional Neural Network

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


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. Schuiteman, “Discovering new orchids,” Kew, 2018. [Online]. Available:
[2] NWS Government. Biodiversity Conservation Act Schedule 6 – Protected Plants. Part 2 Whole Plant. Retrieved on 10 March 2020. Published on the Internet; ts/Licences-and-permits/biodiversity-conservation-act-schedule-6-part-2wh
[3] Govaerts R, Bernet P, Kratochvil K, Gerlach G, Carr G, Alrich P, Pridgeon AM, Pfahl J, Campacci MA, Holland Baptista D, Tigges H, Shaw J, Cribb PJ, George A, Kreuz K, Wood JJ (2017) World checklist of Orchidaceae. The Royal Botanic Gardens, Kew. http:// Accessed on: 2020-03-22.
[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 (2018) A review of the trade in orchids, and its implications for conservation. Bot J Linn Soc 186:435–455
[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, Volume 177, Issue 2, February 2015, Pages 151–174,
[6] Britannica, T. Editors of Encyclopaedia (2018, June 26). Species Plantarum. Encyclopedia Britannica.
[7] D. Mellard, “Orchid Names: The Basics,” 2013. [Online]. Available: [Accessed: 2020].
[8] Ian Goodfellow and Yoshua Bengio and Aaron Courville (2016). Deep Learning. MIT Press. p.326. "CS231n Convolutional Neural Networks for Visual . Recognition”. Retrieved 2019-11-7.
[9] Image Classification Transfer Learning with Inception v3. Retrieved 1 July 2020
[10] Milton-Barker, A. (2019). Inception V3 Deep Convolutional Architecture for Classifying Acute Myeloid/Lymphoblastic Leukemia. Retrieved 2 July 2020, from eption-v3-deep-convolutional-architecture-for-classifying-acute-myeloidlymp hoblastic.html.
How to Cite
MOHTAR, Itaza Afiani; MOHD FADZIL, Annisa Atikah. 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: 26 mar. 2023. doi:

Most read articles by the same author(s)

Obs.: This plugin requires at least one statistics/report plugin to be enabled. If your statistics plugins provide more than one metric then please also select a main metric on the admin's site settings page and/or on the journal manager's settings pages.