Evaluation of features for leaf classification in challenging conditions

Fine-grained leaf classification has concentrated on the use of traditional shape and statistical features to classify ideal images. In this paper we evaluate the effectiveness of traditional hand-crafted features and propose the use of deep convolutional neural network (ConvNet) features. We introduce a range of condition variations to explore the robustness of these features, including: translation, scaling, rotation, shading and occlusion. Evaluations on the Flavia dataset demonstrate that in ideal imaging conditions, combining traditional and ConvNet features yields state-of-the- art performance with an average accuracy of 97 . 3% ± 0 . 6% compared to traditional features which obtain an average accuracy of 91 . 2% ± 1 . 6% . Further experiments show that this combined classification approach consistently outper forms the best set of traditional features by an average of 5 . 7% for all of the evaluated condition variations.