Oso Oluwatobi A, Jayeola Adeniyi A
Plant Anatomy Laboratory, Department of Botany University of Ibadan Oyo State Nigeria.
Present address: Oluwatobi A. Oso, Department of Ecology and Evolutionary Biology Yale University New Haven Connecticut USA.
Appl Plant Sci. 2021 Oct 28;9(9-10):e11448. doi: 10.1002/aps3.11448. eCollection 2021 Sep-Oct.
Plant leaves are one of the most important organs for plant identification due to their variability across different taxonomic groups. While traditional morphometrics has contributed tremendously to reducing the problems accompanying plant identification and morphology-based species delimitation, image-analysis digital solutions have made it easy to detect more characters to complement existing leaf data sets.
Here, we apply MorphoLeaf to generate a morphometric data set from 140 leaf specimens of seven Cucurbitaceae species via landmark extraction, the reparameterization of leaf contours, and data quantification and normalization. A statistical analysis was performed on the resulting data set.
A principal component analysis revealed that leaf blade area, blade perimeter, tooth area, tooth perimeter, the measure of the distance from tooth position to the tip, and the measure of the distance from tooth position to the base are important and informative landmarks that contribute to the variation within the species studied.
MorphoLeaf can be applied to quantitatively track leaf diversity, thereby functionally integrating morphometrics and shape visualization into the digital identification of plants. The success of digital morphometrics in leaf outline analyses presents researchers with opportunities to carry out more accurate image-based research in areas such as plant development, evolution, and phenotyping.
由于植物叶片在不同分类群之间存在差异，因此它是植物识别最重要的器官之一。虽然传统形态计量学在减少植物识别和基于形态的物种划分所伴随的问题方面做出了巨大贡献，但图像分析数字解决方案使人们能够轻松检测到更多特征，以补充现有的叶片数据集。
在此，我们应用MorphoLeaf，通过地标提取、叶片轮廓的重新参数化以及数据量化和归一化，从七个葫芦科物种的140个叶片标本中生成一个形态计量数据集。对所得数据集进行了统计分析。
主成分分析表明，叶片面积、叶片周长、齿面积、齿周长、从齿位置到叶尖的距离测量值以及从齿位置到基部的距离测量值是重要且信息丰富的地标，它们有助于所研究物种内部的变异。
MorphoLeaf可用于定量追踪叶片多样性，从而在功能上将形态计量学和形状可视化整合到植物的数字识别中。数字形态计量学在叶片轮廓分析中的成功为研究人员提供了机会，使其能够在植物发育、进化和表型分析等领域开展更准确的基于图像的研究。
