Plant Identification Via Leaf Classification Using Color and Biometric Features

Abstract

Plants that are of great importance for humans and other living things are an integral part of our ecosystem. In today's world, where many plant species are at risk of disappearance, the identification of plants helps to protect and survive all natural life. There are many studies presented in the literature for plant identification. The most popular of these identification methods is leaf based classification. The reason for choosing leaves in this classification is that they are easier to obtain than other biometric components such as flowers available for a short period of time. Various biometric properties of the leaf must be determined for leaf classifications. In traditionally it is time consuming and expensive to perform this process visually by experts. In this article, various leaf biometric features obtained by digital image processing methods are used as the feature extraction step for automatic leaf classification. As the classification algorithms, Naive Bayes, Linear Regression, Multilayer Perceptron, Decision Tree and Random Forest are used. According to the experimental results using the training set as the test set, 100% recognition rate is obtained for Random Forest classification algorithm and 96% recognition rate is obtained in 30-fold cross validation for Linear Regression classification algorithm.