A Novel Method for Quantifying Plant Morphological Characteristics Using Normal Vectors and Local Curvature Data via 3D Modelling-A Case Study in Leaf Lettuce

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.

Genetic effects of Red Lettuce Leaf genes on red coloration in leaf lettuce under artificial lighting conditions

Some cultivars of lettuce accumulate anthocyanins, which act as functional food ingredients. Leaf lettuce has been known to be erratic in exhibiting red color when grown under artificial light, and there is a need for cultivars that more stably exhibit red color in artificial light cultivation. In this study, we aimed to dissect the genetic architecture for red coloring in various leaf lettuce cultivars grown under artificial light. We investigated the genotype of Red Lettuce Leaf (RLL) genes in 133 leaf lettuce strains, some of which were obtained from publicly available resequencing data. By studying the allelic combination of RLL genes, we further analyzed the contribution of these genes to producing red coloring in leaf lettuce. From the quantification of phenolic compounds and corresponding transcriptome data, we revealed that gene expression level-dependent regulation of RLL1 (bHLH) and RLL2 (MYB) is the underlying mechanism conferring high anthocyanin accumulation in red leaf lettuce under artificial light cultivation. Our data suggest that different combinations of RLL genotypes cause quantitative differences in anthocyanin accumulation among cultivars, and some genotype combinations are more effective at producing red coloration even under artificial lighting.