International Journal of Computer Science & Network Security

국제컴퓨터통신보호논문지학회 (International Journal of Computer Science & Network Security)
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Classification of Plants into Families based on Leaf Texture

  • TREY, Zacrada Francoise (Laboratoire de recherche en informatique et telecommunication, INP-HB) ;
  • GOORE, Bi Tra (Laboratoire de recherche en informatique et telecommunication, INP-HB) ;
  • BAGUI, K. Olivier (Laboratoire Instrumentation Image et Spectroscopie, INP-HB) ;
  • TIEBRE, Marie Solange (Laboratoire de Botanique, Universite Felix Houphouet-Boigny)
  • 투고 : 2021.02.05
  • 발행 : 2021.02.28
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초록

Plants are important for humanity. They intervene in several areas of human life: medicine, nutrition, cosmetics, decoration, etc. The large number of varieties of these plants requires an efficient solution to identify them for proper use. The ease of recognition of these plants undoubtedly depends on the classification of these species into family; however, finding the relevant characteristics to achieve better automatic classification is still a huge challenge for researchers in the field. In this paper, we have developed a new automatic plant classification technique based on artificial neural networks. Our model uses leaf texture characteristics as parameters for plant family identification. The results of our model gave a perfect classification of three plant families of the Ivorian flora, with a determination coefficient (R2) of 0.99; an error rate (RMSE) of 1.348e-14, a sensitivity of 84.85%, a specificity of 100%, a precision of 100% and an accuracy (Accuracy) of 100%. The same technique was applied on Flavia: the international basis of plants and showed a perfect identification regression (R2) of 0.98, an error rate (RMSE) of 1.136e-14, a sensitivity of 84.85%, a specificity of 100%, a precision of 100% and a trueness (Accuracy) of 100%. These results show that our technique is efficient and can guide the botanist to establish a model for many plants to avoid identification problems.

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참고문헌

  1. TREY Zacrada F.O and Al. « Classification des especes vegetales par famille » International Journal of Scientific Research & Engineering Technology (IJSET)Vol.7 pp. 1-5 Copyright IPCO-2019ISSN 1737-9296, 2019
  2. TREY Zacrada F.O and Al «Classification of plant species by similarity using automatic learning» https://doi.org/10.1007/978-3-030-41593-8_14 International Conference on e-Infrastructure and eServices for Developing Countries AFRICOMM 2019: e-Infrastructure and e-Services for Developing Countries pp 186-201, 2020.
  3. Alain Coulon, Artificial Intelligence Supplement, Letter 111 ; Spring
  4. Vijay satti and Al, an automatic leaf recognition system for plant identification using machine vision technology, International Journal of Engineering Science and Technology (IJEST), 2016.
  5. Irie A. Zoro BI and Al. « Caracterisation botanique et agronomique de trois especes de cucurbites consommees en sauce en Afrique de l'Ouest : Citrullus sp., Cucumeropsis mannii Naudin et Lagenaria siceraria (Molina) Standl», BASE [En ligne], Volume 7 (2003), Numero 3-4, 189-199 URL : https://popups.uliege.be/1780-4507/index.php?id=14375, 2003
  6. Munisami and Al, «Plant Leaf Recognition Using Shape Features and Colour Histogram with K-nearest Neighbour Classifiers», Article in Procedia Computer Science, DOI: 10.1016/j.procs.2015.08.095, December 2015.
  7. Arun priya and Al, an efficient leaf recognition algorithm for plant classification using support vector machine, Proceedings of the International Conference on Pattern Recognition, Informatics and Medical Engineering, March 21-23, 2012.
  8. Dimitris G. Tsolakidis and Al., « Plant Leaf Recognition Using Zernike Moments and Histogram of Oriented Gradients », SETN 2014, Springer International Publishing Switzerland 2014, LNCS 8445, pp. 406-417, 2014.
  9. Shubham Lavania, Palash Sushil Matey, Leaf Recognition using Contour based Edge, Detection and SIFT Algorithm, IEEE2002
  10. Alexis Joly and Al, LifeCLEF 2016: Multimedia Life Species Identification Challenges, CLEF: Cross-Language Evaluation Forum, Evora, Portugal. pp.286-310, ff10.1007/978-3-319-44564-9_26ff. ffhal-01373781f, Sep 2016
  11. Abdul Kadir and Al, Leaf Classification Using Shape, Color, and Texture Features, International Journal of Computer Trends and Technology- July to Aug Issue 2011.
  12. Jagadeesh and Al, Image Processing Based Detection of Fungal Diseases in Plants, Procedia Computer Science 46:1802-1808, DOI: 10.1016/j.procs.2015.02.137, December 2015.
  13. Youcef Djeriri, Les Reseaux de Neurones Artificiels, UDL-SBA-2017, 20 September 2017
  14. [a, b] Aimen Aakif and Muhammad Faisal Khan, Automatic classification of plants based on their leaves, ScienceDirect, 2015.
  15. Nobuyuki Otsu and Al, A Threshold Selection Method from Gray-Level Histograms, IEEE Transactions on Systems, Man, and Cybernetics (Volume: 9, Issue: 1, Jan. 1979) DOI: 10.1109/TSMC.1979.4310076, Page(s): 62 - 66
  16. Elise Arnaud - Edmond Boyer, Analyse d'images - introduction, http://perception.inrialpes.fr/people/Boyer/Teaching/L3/
  17. [a, b] Rafael C. Gonzalez, Digital image using MATLAB, chapter 11 p 466.
  18. Wong, C. K. and M. C. Easton. An Efficient Method for Weighted Sampling Without Replacement. SIAM Journal of Computing 9(1), pp.111-113,198 https://doi.org/10.1137/0209009
  19. www.f futura-sciences.com 'Planet' Definitions Alain Coulon, Artificial, Intelligence Supplement, Letter 111; Spring 2018.
  20. Sakshi Kohli, Surbhi Miglani, Rahul Rapariya. basics of artificial neural network, IJCSMC 2014, 3, 745-751.