Sparse Based Automatic Plant Identification System

Abstract

Plant knowledge is crucial for protecting biodiversity. Medicinal plants have been used throughout the human history. Ayurveda is one of the oldest medicine system, which is even recognized in the modern medical society, uses plants for the preparation of medicines. An individual with deep knowledge of plants can only differentiate between these species. This makes leaf identification very difficult. Nowadays, with the rich development in image processing, pattern recognition, and availability of digital cameras, cell phones and the remote access to databases, gives an idea for automatic plant identification using digital image processing.For plant identification shape of a leaf is used because shape and size of the leaf can be easily observed, captured, described and are less affected by seasonal changes. For automatic plant identification, the first step is to extract features from leaf images to be recognized.A leaf image can be described by its, shape texture and color. The color of a leaf may differ with the seasons and climatic conditions, while most plants of leaves have comparable color. In this work, a system is being developed which utilized shape of a leaf for automatic plant identification because leaf shape is universal and can be easily extracted. This system takes input as a leaf image and outputs the name of the species and other relevant details which are stored in the database.The system is designed using the technique of image identification using shape feature sparse representation based classifier. The descriptor used for shape identification described using SIFT. The classification was done using a sparse representation based classifier with the LC-KSVD algorithm. The system was tested for a certain class of leaves and the performance of the system is compared with an existing system.