  There is a warning light for the loss of plant habitats worldwide that
entails concerted efforts to conserve plant biodiversity. Thus, plant species
classification is of crucial importance to address this environmental
challenge. In recent years, there is a considerable increase in the number of
studies related to plant taxonomy. While some researchers try to improve their
recognition performance using novel approaches, others concentrate on
computational optimization for their framework. In addition, a few studies are
diving into feature extraction to gain significantly in terms of accuracy. In
this paper, we propose an effective method for the leaf recognition problem. In
our proposed approach, a leaf goes through some pre-processing to extract its
refined color image, vein image, xy-projection histogram, handcrafted shape,
texture features, and Fourier descriptors. These attributes are then
transformed into a better representation by neural network-based encoders
before a support vector machine (SVM) model is utilized to classify different
leaves. Overall, our approach performs a state-of-the-art result on the Flavia
leaf dataset, achieving the accuracy of 99.58\% on test sets under random
10-fold cross-validation and bypassing the previous methods. We also release
our codes (Scripts are available at
https://github.com/dinhvietcuong1996/LeafRecognition) for contributing to the
research community in the leaf classification problem.
