Plant Leaf Recognition Using Shape Features and Colour Histogram with K-nearest Neighbour Classifiers

Abstract

Abstract Automated systems for plant recognition can be used to classify plants into appropriate taxonomies. Such information can be useful for botanists, industrialists, food engineers and physicians. In this work, a recognition system capable of identifying plants by using the images of their leaves has been developed. A mobile application was also developed to allow a user to take pictures of leaves and upload them to a server. The server runs pre-processing and feature extraction techniques on the image before a pattern matcher compares the information from this image with the ones in the database in order to get potential matches. The different features that are extracted are the length and width of the leaf, the area of the leaf, the perimeter of the leaf, the hull area, the hull perimeter, a distance map along the vertical and horizontal axes, a colour histogram and a centroid-based radial distance map. A k-Nearest Neighbour classifier was implemented and tested on 640 leaves belonging to 32 different species of plants. An accuracy of 83.5% was obtained. The system was further enhanced by using information obtained from a colour histogram which increased the recognition accuracy to 87.3%. Furthermore, our system is simple to use, fast and highly scalable.