Plant Classification Using Weighted k-NN Variants

• Larissa F. S. Britto UFRPE
• Luciano D. S. Pacifico UFRPE

DOI: https://doi.org/10.5753/eniac.2018.4404

Abstract

Automatic plant species identification is a difficulty challenge for botanical taxonomy field. Many works have been proposed towards the development of automatic plant species recognition systems through machine learning methods. One of the most popular algorithms for plant classification is the k-Nearest Neighbor (k-NN), given its simplicity and robustness. In this work, we evaluate the performance of two improved weighted k-NN algorithms when dealing with plant classification task. Experimental evaluation includes three real-world data sets obtained from different image processing and feature extraction processes. Also, a statistical hypothesis test is employed to perform an overall evaluation of the selected models.

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