Plant leaf classification using centroid distance and axis of least inertia method

In this paper, we use centroid distance and axis of least inertia method for plant leaf classification. For this propose the RGB (Red, Green, Blue) image are converted to the binary image. Then, Canny operator is applied to the binary image to recognize the edges of the image before thinning the edges. After that, the boundary of the image is traced to sample the shape. Sampling helps us to avoid time-consuming computations. We compute the centroid distance of these points and distance of sampling points from axis of least inertia line. By selecting a fixed start point and normalizing the distances, the proposed method is shown to be invariant to image transformations (translation, rotation, reflection and scaling) and robust to minor deformations and occlusions. In this study, probabilistic neural network (PNN) has been used as a classifier. Two public leaf datasets including: Swedish leaf dataset and Flavia dataset are evaluated. Experimental results demonstrate the superior performance of the proposed feature in plant leaf classification.