Leaf Bagging: A novel meta heuristic optimization based framework for leaf identification

Automated plant recognition based on leaf images is a challenging task among the researchers from several fields. This task requires distinguishing features derived from leaf images for assigning class label to a leaf image. There are several methods in literature for extracting such distinguishing features. In this paper, we propose a novel automated framework for leaf identification. The proposed framework works in multiple phases i.e. pre-processing, feature extraction, classification using bagging approach. Initially, leaf images are pre-processed using image processing operations such as boundary extraction and cropping. In the feature extraction phase, popular nature inspired optimization algorithms viz. Spider Monkey Optimization (SMO), Particle Swarm Optimization (PSO) and Gray Wolf Optimization (GWO) have been exploited for reducing the dimensionality of features. In the last phase, a leaf image is classified by multiple classifiers and then output of these classifiers is combined using majority voting. The effectiveness of the proposed framework is established based on the experimental results obtained on three datasets i.e. Flavia, Swedish and self-collected leaf images. On all the datasets, it has been observed that the classification accuracy of the proposed method is better than the individual classifiers. Furthermore, the classification accuracy for the proposed approach is comparable to deep learning based method on the Flavia dataset.