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Reseach Article

Identification of Plant Species using Supervised Machine Learning

by Ankita Tripathi, Ravi Datta Sharma, Shrawan Kumar Trivedi
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 182 - Number 13
Year of Publication: 2018
Authors: Ankita Tripathi, Ravi Datta Sharma, Shrawan Kumar Trivedi
10.5120/ijca2018917755

Ankita Tripathi, Ravi Datta Sharma, Shrawan Kumar Trivedi . Identification of Plant Species using Supervised Machine Learning. International Journal of Computer Applications. 182, 13 ( Sep 2018), 6-12. DOI=10.5120/ijca2018917755

@article{ 10.5120/ijca2018917755,
author = { Ankita Tripathi, Ravi Datta Sharma, Shrawan Kumar Trivedi },
title = { Identification of Plant Species using Supervised Machine Learning },
journal = { International Journal of Computer Applications },
issue_date = { Sep 2018 },
volume = { 182 },
number = { 13 },
month = { Sep },
year = { 2018 },
issn = { 0975-8887 },
pages = { 6-12 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume182/number13/29920-2018917755/ },
doi = { 10.5120/ijca2018917755 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:11:16.930779+05:30
%A Ankita Tripathi
%A Ravi Datta Sharma
%A Shrawan Kumar Trivedi
%T Identification of Plant Species using Supervised Machine Learning
%J International Journal of Computer Applications
%@ 0975-8887
%V 182
%N 13
%P 6-12
%D 2018
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This research emphasizes on the plant species recognition which is considered as an important area of research in plant biotechnology. Artificial intelligence and machine learning have a prominent place in such research. In this study, a boosted evolutionary plant species classifier has been developed that works on ensemble of classifier methods. This classifier identifies different species of plants with the help of different texture and shape features of leaf image. A publicly available plant image dataset has been incorporated where features are extracted with the help of image processing tools. The proposed classifier is trained and tested with the help of these features. Further, proposed classifier is compared with other popular machine learning classifier viz. Bayesian, Naïve Bayes, SVM, J48, Random forest, Genetic Programming. Proposed evolutionary classifier was found to be good in terms of F-Value, FP rate and TP rate whereas SVM was found to be underperforming predictor in this study. However, the training time of the proposed classifier was high.

References
  1. Rao, P. V., & Gan, S. H. (2014). Cinnamon: a multifaceted medicinal plant. Evidence-Based Complementary and Alternative Medicine, 2014.
  2. Estimated Number of Animal and Plant Species on Earth" Fact Monster. 2000–2013 Sandbox Networks, Inc., publishing as Fact Monster. 18 Apr. 2017,https://www.factmonster.com/science/animals/estimated-number-animal-and-plant-species-earth/
  3. Kadir, A., Nugroho, L. E., Susanto, A., & Santosa, P. I. (2013). Leaf classification using shape, color, and texture features. arXiv preprint arXiv:1401.4447.
  4. Brantes Ferreira, J., Zanela Klein, A., Freitas, A., & Schlemmer, E. (2013). Mobile learning: Definition, uses and challenges. In Increasing student engagement and retention using mobile applications: Smartphones, skype and texting technologies (pp. 47-82). Emerald Group Publishing Limited.
  5. Du J.-X., Wang X.-F., Zhang G.-J. 2007. Leaf shape based plant species recognition. Applied Mathematics and Computation 185: 883–893
  6. Nam, Yun young, Eenjun Hwang, and Dongyoon Kim. "CLOVER: a mobile content-based leaf image retrieval system." In International Conference on Asian Digital Libraries, pp. 139-148. Springer Berlin Heidelberg, 2005.
  7. Jensen, John R., and Kalmesh Lulla. "Introductory digital image processing: a remote sensing perspective." (1987): 65-65.
  8. Hemming, J., & Rath, T. (2001). Computer-vision-based weed identification under field conditions using controlled lighting. Journal of agricultural engineering research, 78(3), 233-244.
  9. Pedro F.B. Silva, Andre R.S. Marcal, Rubim M. Almeida da Silva (2013)Evaluation of Features for Leaf Discriminatio, n',. Springer Lecture Notes in Computer Science, Vol. 7950, 197-204
  10. P Anderson, R., Dudík, M., Ferrier, S., Guisan, A., J Hijmans, R., Huettmann, F., ... & A Loiselle, B. (2006). Novel methods improve prediction of species’ distributions from occurrence data. Ecography, 29(2), 129-151.
  11. Guisan, A., Edwards, T. C., & Hastie, T. (2002). Generalized linear and generalized additive models in studies of species distributions: setting the scene. Ecological modelling, 157(2), 89-100.
  12. Skowronek, S., Asner, G. P., & Feilhauer, H. (2017). Performance of one-class classifiers for invasive species mapping using airborne imaging spectroscopy. Ecological Informatics, 37, 66-76.
  13. Trivedi, S. K., & Dey, S. (2013, December). An Enhanced Genetic Programming Approach for Detecting Unsolicited Emails. In Computational Science and Engineering (CSE), 2013 IEEE 16th International Conference on (pp. 1153-1160). IEEE.
  14. J.R. Koza, Genetic Programming: on the Programming of Computers by Means of Natural Selection, MIT Press, Cambridge, MA, 1992.
  15. J.K. Kishore, L.M. Patnaik, V. Mani, V.K. Agrawal, Application of genetic programming for multi-category pattern classification, IEEE Trans. Evol. Comput. 4 (3) (2000) 242–258.
  16. Trivedi, S. K., Dey, S., & Dey, S. (2016). A novel committee selection mechanism for combining classifiers to detect unsolicited emails. VINE Journal of Information and Knowledge Management Systems, 46(4), 524-548.
  17. Lewis, D. D. (1998). Naive (Bayes) at forty: The independence assumption in information retrieval. In Machine learning: ECML-98(pp. 4-15). Springer Berlin Heidelberg.
  18. Tripathi, A., & Trivedi, S. K. (2016, October). Sentiment analyis of Indian movie review with various feature selection techniques. In Advances in Computer Applications (ICACA), IEEE International Conference on (pp. 181-185). IEEE.
  19. V.N Vapnik, “An Overview of Statistical Learning Theory”, IEEE Trans.on Neural Network, Vol. 10, No. 5, pp.988-998 , 1999. 6
  20. Bhargava, N., Sharma, G., Bhargava, R., & Mathuria, M. (2013). Decision tree analysis on j48 algorithm for data mining. Proceedings of International Journal of Advanced Research in Computer Science and Software Engineering, 3(6).
  21. Liaw, A., & Wiener, M. (2002). Classification and regression by randomForest. R news, 2(3), 18-22.
  22. Trivedi, S. K., & Dey, S. (2014). Interaction between feature subset selection techniques and machine learning classifiers for detecting unsolicited emails.
  23. Provost, F., & Fawcett, T. (2001). Robust classification for imprecise environments. Machine learning, 42(3), 203-231.
  24. Bradley, A. P. (1997). The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern recognition, 30(7), 1145-1159.
  25. Moreno-Torres, J. G., Raeder, T., Alaiz-RodríGuez, R., Chawla, N. V., & Herrera, F. (2012). A unifying view on dataset shift in classification. Pattern Recognition, 45(1), 521-530.
  26. Woolson, R. F. (2007). Wilcoxon signed‐rank test. Wiley encyclopedia of clinical trials, 1-3.
  27. Demšar, J. (2006). Statistical comparisons of classifiers over multiple data sets. Journal of Machine learning research, 7(Jan), 1-30.
  28. ACM SIGAPP Applied Computing Review, 14(1), 53-61.
Index Terms

Computer Science
Information Sciences

Keywords

Plant Species Leaf image Genetic programming Machine learning F-Value FP rate Training time.