Department Biogeochemical Integration, Max Planck Institute for Biogeochemistry, Jena, Germany.
Data-intensive Systems and Visualization Group, Technische Universität Ilmenau, Ilmenau, Germany.
Plant J. 2024 Nov;120(4):1343-1357. doi: 10.1111/tpj.17053. Epub 2024 Oct 9.
Plant leaves play a pivotal role in automated species identification using deep learning (DL). However, achieving reproducible capture of leaf variation remains challenging due to the inherent "black box" problem of DL models. To evaluate the effectiveness of DL in capturing leaf shape, we used geometric morphometrics (GM), an emerging component of eXplainable Artificial Intelligence (XAI) toolkits. We photographed Ranunculus auricomus leaves directly in situ and after herbarization. From these corresponding leaf images, we automatically extracted DL features using a neural network and digitized leaf shapes using GM. The association between the extracted DL features and GM shapes was then evaluated using dimension reduction and covariation models. DL features facilitated the clustering of leaf images by source populations in both in situ and herbarized leaf image datasets, and certain DL features were significantly associated with biological leaf shape variation as inferred by GM. DL features also enabled leaf classification into morpho-phylogenomic groups within the intricate R. auricomus species complex. We demonstrated that simple in situ leaf imaging and DL reproducibly captured leaf shape variation at the population level, while combining this approach with GM provided key insights into the shape information extracted from images by computer vision, a necessary prerequisite for reliable automated plant phenotyping.
植物叶片在基于深度学习（DL）的自动化物种识别中起着至关重要的作用。然而，由于 DL 模型的固有“黑箱”问题，实现叶片变化的可重复捕获仍然具有挑战性。为了评估 DL 在捕捉叶片形状方面的有效性，我们使用了几何形态测量学（GM），这是可解释人工智能（XAI）工具包的一个新兴组成部分。我们直接在原地和标本采集后拍摄毛茛属 auricomus 叶片的照片。从这些对应的叶片图像中，我们使用神经网络自动提取 DL 特征，并使用 GM 对叶片形状进行数字化。然后，使用降维和协变模型评估提取的 DL 特征与 GM 形状之间的关联。DL 特征促进了来源于源种群的叶片图像聚类，无论是在原地还是标本采集后的叶片图像数据集中，某些 DL 特征与 GM 推断的生物叶片形状变化显著相关。DL 特征还能够将叶片分类为错综复杂的毛茛属 auricomus 物种复合体中的形态 - 系统发育组。我们证明了简单的原地叶片成像和 DL 可以在种群水平上可重复地捕获叶片形状变化，而将这种方法与 GM 相结合为计算机视觉从图像中提取的形状信息提供了关键见解，这是可靠的自动化植物表型分析的必要前提。
