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14 August 2019 Initial investigation of different classifiers for plant leaf classification using multiple features
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Proceedings Volume 11179, Eleventh International Conference on Digital Image Processing (ICDIP 2019); 1117922 (2019) https://doi.org/10.1117/12.2539654
Event: Eleventh International Conference on Digital Image Processing (ICDIP 2019), 2019, Guangzhou, China
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.
© (2019) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Qi Zhang, Shaoning Zeng, and Bob Zhang "Initial investigation of different classifiers for plant leaf classification using multiple features", Proc. SPIE 11179, Eleventh International Conference on Digital Image Processing (ICDIP 2019), 1117922 (14 August 2019); https://doi.org/10.1117/12.2539654
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