Complex Identification of Plants from Leaves

  • Jair CervantesEmail author
  • Farid Garcia Lamont
  • Lisbeth Rodriguez Mazahua
  • Alfonso Zarco Hidalgo
  • José S. Ruiz Castilla
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10956)


The automatic identification of plant leaves is a very important current topic of research in vision systems. Several researchers have tried to solve the problem of identification from plant leaves proposing various techniques. The proposed techniques in the literature have obtained excellent results on data sets where the leaves have dissimilar features to each other. However, in cases where the leaves are very similar to each other, the classification accuracy falls significantly. In this paper, we proposed a system to deal with the performance problem of machine learning algorithms where the leaves are very similar. The results obtained show that combination of different features and features selection process can improve the classification accuracy.


Plant identification Vision system Features selection 



This study was funded by the Research Secretariat of the Autonomous University of the State of Mexico with the research project 5228/2018/CI.


  1. 1.
    Huang, Y., Lan, Y., Hoffmann, W.C.: Use of airborne multi-spectral imagery for area wide pest management. Agric. Eng. Int. CIGR Ejournal Manuscr. IT 07(010), 1–14 (2008)Google Scholar
  2. 2.
    Singh, V., Misra, A.K.: Detection of plant leaf diseases using image segmentation and soft computing techniques. Inf. Process. Agric. 4(1), 41–49 (2017). ISSN:2214-3173Google Scholar
  3. 3.
    Ferentinos, K.P.: Deep learning models for plant disease detection and diagnosis. Comput. Electron. Agric. 145, 311–318 (2018). ISSN:01681699CrossRefGoogle Scholar
  4. 4.
    Du, J.X., Wang, X.F., Zhang, G.J.: Leaf shape based plant species recognition. Appl. Math. Comput. 185(2), 883–893 (2007)zbMATHGoogle Scholar
  5. 5.
    Sampallo, G.: Reconocimiento de tipos de hojas. Inteligencia Artificial. Rev. Iberoam. Intel. Artif. 7(21), 55–62 (2003)Google Scholar
  6. 6.
    Cerutti, G., Tougne, L., Mille, J., Vacavant, A., Coquin, D.: Understanding leaves in natural images - a model-based approach for tree species identification. Comput. Vis. Image Underst. 117(10), 1482–1501 (2013)CrossRefGoogle Scholar
  7. 7.
    Larese, M.G., Namías, R., Craviotto, R.M., Arango, M.R., Gallo, C., Granitto, P.M.: Automatic classification of legumes using leaf vein image features. Pattern Recognit. 47(1), 158–168 (2014)CrossRefGoogle Scholar
  8. 8.
    Chaki, J., Parekh, R.: Designing an automated system for plant leaf recognition. Int. J. Adv. Eng. Technol. 2(1), 149–158 (2012)Google Scholar
  9. 9.
    Park, J.-S., Kim, T.Y.: Shape-Based Image Retrieval Using Invariant Features. In: Aizawa, K., Nakamura, Y., Satoh, S. (eds.) PCM 2004. LNCS, vol. 3332, pp. 146–153. Springer, Heidelberg (2004). Scholar
  10. 10.
    Kumar N., Belhumeur P.N., Biswas A.: Leafsnap: a computer vision system for automatic plant species identification. In: Proceedings of the ECCV 2012, pp. 502–516 (2012)CrossRefGoogle Scholar
  11. 11.
    Novotny, P., Suk, T.: Leaf recognition of woody species in Central Europe. Biosyst. Eng. 115(4), 444–452 (2013)CrossRefGoogle Scholar
  12. 12.
    Husin, Z., Shakaff, A.Y.M., Aziz, A.H.A., Farook, R.S.M., Jaafar, M.N., Hashim, U., Harun, A.: Embedded portable device for herb leaves recognition using image processing techniques and neural network algorithm. Comput. Electron. Agric. 89, 18–29 (2012)CrossRefGoogle Scholar
  13. 13.
    Liu, N., Kan, J.-m.: Improved deep belief networks and multi-feature fusion for leaf identification. Neurocomputing 216, 460–467 (2016). ISSN:0925-2312CrossRefGoogle Scholar
  14. 14.
    VijayaLakshmi, B., Mohan, V.: Kernel-based PSO and FRVM: an automatic plant leaf type detection using texture, shape, and color features. Comput. Electron. Agric. 125, 99–112 (2016). ISSN:0168-1699CrossRefGoogle Scholar
  15. 15.
    Tico, M., Haverinen, T., Kuosmanen, P.: A method of color histogram creation for image retrieval. In: Proceedings of the Nordic Signal Processing Symposium (NORSIG-2000), Kolmarden, Sweden, pp. 157–160 (2000)Google Scholar
  16. 16.
    Cope, J., Corney, D., Clark, J., Remagnino, P., Wilkin, P.: Plant species identification using digital morphometrics: a review. Expert Syst. Appl. 39(8), 7562–7573 (2012)CrossRefGoogle Scholar
  17. 17.
    He, D.C., Wang, L.: Texture unit, texture spectrum, and texture analysis. IEEE Trans. Geosci. Remote Sens. 28, 509–512 (1990)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Jair Cervantes
    • 1
    Email author
  • Farid Garcia Lamont
    • 1
  • Lisbeth Rodriguez Mazahua
    • 2
  • Alfonso Zarco Hidalgo
    • 1
  • José S. Ruiz Castilla
    • 1
  1. 1.Posgrado e InvestigaciónUAEMEX (Autonomous University of Mexico State)TexcocoMexico
  2. 2.Division of Research and Postgraduate StudiesInstituto Tecnológico de OrizabaOrizabaMexico

Personalised recommendations