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.

中文翻译：

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用于叶种识别的视觉描述符和分类技术综述

植物对生命至关重要。植物科学的关键研究领域包括植物物种识别、使用高光谱图像进行杂草分类、监测植物健康和追踪叶片生长，以及叶片信息的语义解释。植物学家通过区分叶子的形状、尖端、基部、叶缘和叶脉，以及叶子的质地和复叶小叶的排列，很容易识别植物种类。由于对专家的需求不断增加并呼吁生物多样性，因此需要智能系统来识别和表征叶子，以便仔细检查特定物种、影响它们的疾病、叶子生长模式等。我们回顾了叶子特征提取中的几种图像处理方法，鉴于特征提取是计算机视觉中的一项关键技术。由于计算机无法理解图像，因此需要通过单独分析图像形状、颜色、纹理和时刻来将它们转换为特征。看起来相同的图像可能在几何和光度变化方面有所不同。在我们的研究中，我们还讨论了某些机器学习分类器，用于分析不同种类的叶子。