Breeding Big Data Management and Utilization Group, Division of Smart Breeding Research, Institute of Crop Science, National Agriculture and Food Research Organization (NARO), Tsukuba 305-0856, Japan.
Research Center for Agricultural Robotics, Core Technology Research Headquarters, NARO, Tsukuba 305-0856, Japan.
Sensors (Basel). 2023 Jul 31;23(15):6825. doi: 10.3390/s23156825.
Three-dimensional measurement is a high-throughput method that can record a large amount of information. Three-dimensional modelling of plants has the possibility to not only automate dimensional measurement, but to also enable visual assessment to be quantified, eliminating ambiguity in human judgment. In this study, we have developed new methods that could be used for the morphological analysis of plants from the information contained in 3D data. Specifically, we investigated characteristics that can be measured by scale (dimension) and/or visual assessment by humans. The latter is particularly novel in this paper. The characteristics that can be measured on a scale-related dimension were tested based on the bounding box, convex hull, column solid, and voxel. Furthermore, for characteristics that can be evaluated by visual assessment, we propose a new method using normal vectors and local curvature (LC) data. For these examinations, we used our highly accurate all-around 3D plant modelling system. The coefficient of determination between manual measurements and the scale-related methods were all above 0.9. Furthermore, the differences in LC calculated from the normal vector data allowed us to visualise and quantify the concavity and convexity of leaves. This technique revealed that there were differences in the time point at which leaf blistering began to develop among the varieties. The precise 3D model made it possible to perform quantitative measurements of lettuce size and morphological characteristics. In addition, the newly proposed LC-based analysis method made it possible to quantify the characteristics that rely on visual assessment. This research paper was able to demonstrate the following possibilities as outcomes: (1) the automation of conventional manual measurements, and (2) the elimination of variability caused by human subjectivity, thereby rendering evaluations by skilled experts unnecessary.
三维测量是一种高通量的方法，可以记录大量信息。植物的三维建模不仅有可能实现尺寸的自动化测量，还能够使视觉评估得以量化，从而消除人为判断的模糊性。在本研究中，我们开发了新的方法，可以从 3D 数据中包含的信息来进行植物的形态分析。具体来说，我们研究了可以通过尺度（维度）和/或人类视觉评估来测量的特征。后者在本文中是特别新颖的。可以在尺度相关维度上测量的特征是基于边界框、凸包、柱状实体和体素来测试的。此外，对于可以通过视觉评估来评估的特征，我们提出了一种使用法向量和局部曲率（LC）数据的新方法。对于这些检查，我们使用了我们高度精确的全方位 3D 植物建模系统。手动测量与尺度相关方法之间的决定系数均高于 0.9。此外，从法向量数据计算出的 LC 差异使得我们能够可视化和量化叶片的凹度和凸度。该技术表明，品种之间叶片起泡开始发展的时间点存在差异。精确的 3D 模型使得对生菜大小和形态特征进行定量测量成为可能。此外，新提出的基于 LC 的分析方法使得对依赖于视觉评估的特征进行量化成为可能。本研究论文能够展示以下可能性作为成果：（1）传统手动测量的自动化，以及（2）消除由人为主观性引起的可变性，从而不需要熟练专家进行评估。
