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Identification of Plant Species using Supervised Machine Learning

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2018
Ankita Tripathi, Ravi Datta Sharma, Shrawan Kumar Trivedi

Ankita Tripathi, Ravi Datta Sharma and Shrawan Kumar Trivedi. Identification of Plant Species using Supervised Machine Learning. International Journal of Computer Applications 182(13):6-12, September 2018. BibTeX

	author = {Ankita Tripathi and Ravi Datta Sharma and Shrawan Kumar Trivedi},
	title = {Identification of Plant Species using Supervised Machine Learning},
	journal = {International Journal of Computer Applications},
	issue_date = {September 2018},
	volume = {182},
	number = {13},
	month = {Sep},
	year = {2018},
	issn = {0975-8887},
	pages = {6-12},
	numpages = {7},
	url = {},
	doi = {10.5120/ijca2018917755},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}


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.


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