Sinuosity coefficients for leaf shape characterisation

Abstract

The design of an efficient and automated model for plant recognition and classification will give the possibility to people with little or no botanical knowledge to conduct field work. In this paper a feature for leaf shape analysis is presented: the Sinuosity coefficients. This new feature is based on the sinuosity measures, which is a value expressing the degree of meandering of a curve. The sinuosity coefficients is a vector of sinuosity measures characterising a given shape. The proposed shape feature is translation and scale invariant. This feature achieved a classification rate of 92% with the Multilayer Perceptron (MLP) classifier, a rate of 91% with the KNearest Neighbour (KNN) and a rate of 94% with the Naive Bayes classifier, on a set of leaf images from the FLAVIA dataset.