Automated analysis of visual leaf shape features for plant classification

Abstract A large number of studies have been performed during the past few years to automatically identify the plant type in a given image. Besides common object recognition difficulties arising mainly due to light, pose and orientation variations, the plant type identification problem is further complicated by the differences in leaf shape overage and changing leaf color under different weather conditions. The limited accuracy of existing approaches can be improved using an appropriate selection of representative leaf based features. This study evaluates different handcrafted visual leaf features, their extraction techniques, and classification methods. Towards this end, a new five-step algorithm is presented (comprising image pre-processing, segmentation, feature extraction, dimensionality reduction, and classification steps) for recognition of plant type through leaf images. The proposed algorithm is evaluated on a publicly available standard dataset ‘Flavia’ of 1600 leaf images and on a self-collected dataset of 625 leaf images. With the proposed algorithm, different classifiers such as k-nearest neighbor (KNN), decision tree, naive Bayes, and multi-support vector machines (SVM) are tested. The best performing KNN, claimed for the final results, reveals that the proposed algorithm gives precision and recall values of 97.6% and 98.8% respectively when tested on ‘Flavia’ dataset. The proposed technique is also tested on our self-collected dataset, giving respectively 96.1% and 97.3% precision and recall measure results. Results confirm that our approach, when augmented with efficient segmentation techniques on raw leaf images, can be a significantly accurate plant type recognition method in practical situations. AlexNet, a Convolutional Neural Network (CNN) based approach is also compared for classification on the datasets as oppose to handcrafted feature-based approach and it is found that the later outperforms the former in robustness when the training dataset is small.

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