Automatic Plant Species Identification Using Image Processing Techniques

No Thumbnail Available



Journal Title

Journal ISSN

Volume Title


Addis Ababa University


Plants are one of the important things that plays a very essential role for all living beings exists on earth. Plants form a fundamental part of life on Earth, providing us with breathable oxygen, food, fuel, medicine and more. Plants also help to regulate the climate, provide habitats and food for insects and other animals. But due to unawareness and environment deterioration, many plants are at the verge of extinction. Understanding of plant behavior and ecology is very important for human being and the entire planet. Plants possess unique features in their leaf that distinguish them from others. Taxonomists use these unique features to classify and identify plant species. However, there is a shortage of such skilled subject matter experts, as well as a limit on financial resources. Several leaf image based plant species identification methods have been proposed to address plant identification problem. However, most methods are inaccurate. Invariant moments that are used for leaf shape features extraction are inadequate. Hu moments are inadequate when leaves from different species have very similar shape. The computation of Zernike moments involve discrete approximation of its continuous integral term which result in loss of information. Hence, it is extremely important to look for an improved method of plant species classification and identification using image processing techniques. In this work a new method based on combined leaf shape and texture features using a class of ensemble method called Random Forest for the classification and identification of plant species has been proposed. Morphological features and Radial Chebyshev moments are extracted from the leaf shape and Gabor filters are extracted from leaf texture. These three features are combined, important features are selected to form a feature set that trained the Random Forest classifier. The Random Forest was trained with 1907 sample leaves of 32 different plant species that are taken form Flavia dataset. The proposed approach is 97% accurate using Random Forest classifier.



Plant Species Identification, Morphological Features, Radial Cheybshev Moments, Gabor Filter, Random Forest