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Initial investigation of different classifiers for plant leaf classification using multiple features
Author(s): Qi Zhang; Shaoning Zeng; Bob Zhang
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Paper Abstract

Plant leaf species classification is an active research area at present with many scientists attempting to use different classifiers with different leaf features to solve it. In this paper we evaluate 10 common classifiers: k-Nearest Neighbors (KNN), support vector machine (SVM), nu-SVM, decision tree, random forest, naïve bayes, linear discriminant analysis (LDA), logistic regression, quadratic discriminant analysis (QDA) and sparse representation in leaf species classification with different leaf features such as shape, texture and margin. Besides this, different numbers of leaf species and training samples for different classifiers were also evaluated in this study. The comprehensive results indicate that random forest, followed by LDA, logistic regression and sparse representation are the most robust and accurate classifiers in leaf recognition using various features.

Paper Details

Date Published: 14 August 2019
PDF: 9 pages
Proc. SPIE 11179, Eleventh International Conference on Digital Image Processing (ICDIP 2019), 1117922 (14 August 2019); doi: 10.1117/12.2539654
Show Author Affiliations
Qi Zhang, Univ. of Macau (China)
Shaoning Zeng, Univ. of Macau (China)
Bob Zhang, Univ. of Macau (China)


Published in SPIE Proceedings Vol. 11179:
Eleventh International Conference on Digital Image Processing (ICDIP 2019)
Jenq-Neng Hwang; Xudong Jiang, Editor(s)

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