Between the 1960s and the present day, the use of morphology in plant taxonomy suffered a major decline, in part driven by the apparent superiority of DNA-based approaches to data generation. However, in recent years computer image recognition has re-kindled the interest in morphological techniques. Linear or geometric morphometric approaches have been employed to distinguish and classify a wide variety of organisms; each has strengths and weaknesses. Here we review these approaches with a focus on plant classification and present a case for the combination of morphometrics with statistical/machine learning. There is a large collection of classification techniques available for biological analysis and selecting the most appropriate one is not trivial. Performance should be evaluated using standardised metrics such as accuracy, sensitivity, and specificity. The gathering and storage of high-resolution images, combined with the processing power of desktop computers, makes morphometric approaches practical as a time- and cost-efficient way of non-destructive identification of plant samples.

中文翻译：

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灰姑娘学科：形态测量学及其在植物分类中的应用

从 1960 年代到现在，形态学在植物分类学中的应用大幅下降，部分原因是基于 DNA 的数据生成方法的明显优势。然而，近年来计算机图像识别重新点燃了对形态学技术的兴趣。线性或几何形态测量方法已被用于区分和分类各种各样的生物。每个人都有优点和缺点。在这里，我们以植物分类为重点回顾了这些方法，并展示了形态测量学与统计/机器学习相结合的案例。有大量分类技术可用于生物分析，选择最合适的分类技术并非易事。应该使用标准化的指标来评估性能，例如准确性、灵敏度、和特异性。高分辨率图像的收集和存储，结合台式计算机的处理能力，使形态测量方法成为一种实用的植物样本无损识别时间和成本效益的方法。