Leaf classification using convexity measure of polygons

Abstract

Plant taxonomy is a long-standing practice in botany. It uses the morphology of plant leaves to make categories. Leaf shape is one of the physical characteristics used to discriminate between plant species. This paper presents the characterisation of a leaf shape using the Convexity Measure of Polygons and the seven invariant moments in combination with other morphological features to improve leaf classification. The Convexity Measure of Polygons used in this paper is based on the minimum ratio obtained by dividing the rotated leaf-bounding perimeter of the associate bounding rectangle of the leaf shape. The proposed model is rotation, translation and scale invariant. It achieves a classification rate of 92% on 400 leaves of 20 species, 99% on 100 leaves of 4 species and 95% on 1600 leaves of 32 species using a Multilayer Perceptron classifier. The proposed method out-performs several state-of-the-art methods when tested under the similar conditions, even with deformed leaves.