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.
翻译:全世界植物生境的丧失有警示灯光,这需要一致努力保护植物生物多样性。因此,植物物种分类对于应对这一环境挑战至关重要。近年来,与植物分类有关的研究数量大幅增加。一些研究人员试图利用新办法提高它们的认知性,而另一些研究人员则侧重于其框架的计算优化。此外,一些研究正在潜入特征提取中,以便从准确性中获得显著收益。在本文件中,我们提出了叶子识别问题的有效方法。在我们提议的方法中,叶子经过一些预处理,以提取其精细的彩色图像、血管图象、XY射影直方图、手制形状、纹理特征和Fourier代针。这些属性随后被转化成一个更好的基于神经网络的摄像师在支持矢量机(SVM)模型(SVM)用于对不同的叶进行分类。总体而言,我们的方法在Flavia叶数据集上展示了一种最先进的结果,在随机的10倍交叉校正/Requal 群落中实现了99.58 ⁇ 的精确度。我们还在以往的研究中发布了一种方法。