Advertisement

Machine Learning Comparative Analysis for Plant Classification

  • Elbrus ImanovEmail author
  • Abdallah Khaled Alzouhbi
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 896)

Abstract

Nowadays, digital image processing, artificial neural network and machine visualization have been pettishly progressing, and they cover a significant side of artificial cleverness and the rule among human beings and electro-mechanical devices. These technologies have been utilized in a wide range of agricultural operations, medicine and manufacturing. By this research the preparation of some functions has been conducted.

In this paper we introduce the classification of maize leaves from pictures that reveal many conditions, opening among pictures, by pre-processing, taking out, plant feature recognition, matching and training, and lastly getting the outcomes executed by Matlab, neural network pattern recognition application. These given features are separated to leaf maturity and picture interpretations, rotary motions and calibration, and they are calculated to develop an approach that gives us better classification algorithm results. A plant scientist may be introduced with a plant for recognition of its classes revealed in its natural home ground, to gather an in-depth recognition.

Keywords

Artificial neural network Digital image processing Machine visualization classification K-nearest neighbor Support vector machine Machine learning 

References

  1. 1.
    Aliev, R.A., Fazlollahi, B., Aliev, R.R.: Soft Computing and its Application in Business and Economics, p. 388. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  2. 2.
    Gouk, H.G., Blake, A.M.: Fast sliding window classification with convolutional neural network. In: 2014 Proceedings of the 29th International Conference on Image and Vision Computing, Hamilton, New Zealand, pp. 114–118 (2014)Google Scholar
  3. 3.
    Karray, F.O., de Silva, C.: Soft Computing and Intelligent Systems Design, pp. 4–13, 223–224. Pearson Education Limited/British Library (2004)Google Scholar
  4. 4.
    Comaniciu, D., Ramesh, V., Meer, P.: Real time tracking of non-rigid objects using mean shift. In: 2000 Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 142–149 (2000)Google Scholar
  5. 5.
    Treisman, A.M., Gelade, G.: A feature integration theory of attention. Cogn. Psychol. 12, 97–136 (1980)CrossRefGoogle Scholar
  6. 6.
    Dallwitz, M.J.: A general system for coding taxonomic descriptions. Taxon 29, 6 (1980)CrossRefGoogle Scholar
  7. 7.
    Du, J.X., Wang, X.F., Zhang, G.J.: Leaf shape based plant species recognition. Appl. Math. Comput. 185, 11 (2007)MathSciNetzbMATHGoogle Scholar
  8. 8.
    Kim, J., Kim, B.S., Savarese, S.: Comparing image classification methods, K-nearest neighbor and support vector machines. In: 2012 Proceedings of the American Conference on Applied Mathematics, pp. 133–138 (2012)Google Scholar
  9. 9.
    Burgers, C.J.C.: A tutorial on supper vector machines for pattern recognition. Data Min. Knowl. Discov. 2, 121–167 (1998)CrossRefGoogle Scholar
  10. 10.
    Tran, Q.A., Zhang, Q.L., Li, X.: Reduce the number of support vectors by using clustering techniques. In: 2003 Proceedings of the Second International Conference on Machine Learning Cybernetics, Xi’an, pp. 1243–1248 (2003)Google Scholar
  11. 11.
    Bermmert, D., Demaine, E., Erickson, J., Longermans, S., Morin, P., Toussaint, G.: Output sensitive algorithms for computing nearest neighbors decision boundaries. Discrete Comput. Geom. 33, 583–604 (2005)Google Scholar
  12. 12.
    Pujari, J.D., Yakkundimath, R., Byadgi, A.S.: Grading and classification of anthracnose fungal disease of fruits based on statistical texture features. Int. J. Adv. Sci. Technol. 52, 121–132 (2013)Google Scholar
  13. 13.
    Ding, C., He, X.: K-means clustering via principal component analysis. In: 2004 Proceedings of International Conference on Machine Learning, pp. 225–232 (2004)Google Scholar
  14. 14.
    Alzouhbi, A.: Plant classification using SVM and KNN classifiers. Thesis Nicosia (2017)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Computer EngineeringNear East UniversityMersin 10Turkey
  2. 2.Department of Water RecyclingMachha, AkkarLebanon

Personalised recommendations