论文标题
叶子物种识别中使用的视觉描述符和分类技术的综述
A Review of Visual Descriptors and Classification Techniques Used in Leaf Species Identification
论文作者
论文摘要
植物对生命至关重要。植物科学领域的关键研究领域包括植物物种识别，使用超光谱图像进行杂草分类，监测植物健康和追踪叶片生长以及叶子信息的语义解释。植物学家通过区分叶子，尖端，底，叶缘和叶静脉的形状以及叶片的质地以及化合物叶片的小叶的排列来轻松识别植物物种。由于对专家的需求不断增长和对生物多样性的呼吁，因此需要识别和表征叶子的智能系统，以仔细检查特定物种，影响它们的疾病，叶片生长的模式等。鉴于特征提取是计算机视觉中的至关重要技术，我们在叶子的特征提取中回顾了几种图像处理方法。由于计算机无法理解图像，因此必须通过单独分析图像形状，颜色，纹理和时刻来将它们转换为功能。看起来相同的图像可能会偏离几何和光度变化。在我们的研究中，我们还讨论了某些机器学习分类器，以分析不同种类的叶子。
Plants are fundamentally important to life. Key research areas in plant science include plant species identification, weed classification using hyper spectral images, monitoring plant health and tracing leaf growth, and the semantic interpretation of leaf information. Botanists easily identify plant species by discriminating between the shape of the leaf, tip, base, leaf margin and leaf vein, as well as the texture of the leaf and the arrangement of leaflets of compound leaves. Because of the increasing demand for experts and calls for biodiversity, there is a need for intelligent systems that recognize and characterize leaves so as to scrutinize a particular species, the diseases that affect them, the pattern of leaf growth, and so on. We review several image processing methods in the feature extraction of leaves, given that feature extraction is a crucial technique in computer vision. As computers cannot comprehend images, they are required to be converted into features by individually analysing image shapes, colours, textures and moments. Images that look the same may deviate in terms of geometric and photometric variations. In our study, we also discuss certain machine learning classifiers for an analysis of different species of leaves.
