MowJoe: a method for automated-high throughput dissected leaf phenotyping

Intra-leaf modeling of Cannabis leaflet shape produces leaf models that predict genetic and developmental identities

The iconic, palmately compound leaves of Cannabis have attracted significant attention in the past. However, investigations into the genetic basis of leaf shape or its connections to phytochemical composition have yielded inconclusive results. This is partly due to prominent changes in leaflet number within a single plant during development, which has so far prevented the proper use of common morphometric techniques. Here, we present a new method that overcomes the challenge of nonhomologous landmarks in palmate, pinnate, and lobed leaves, using Cannabis as an example. We model corresponding pseudo-landmarks for each leaflet as angle-radius coordinates and model them as a function of leaflet to create continuous polynomial models, bypassing the problems associated with variable number of leaflets between leaves. We analyze 341 leaves from 24 individuals from nine Cannabis accessions. Using 3591 pseudo-landmarks in modeled leaves, we accurately predict accession identity, leaflet number, and relative node number. Intra-leaf modeling offers a rapid, cost-effective means of identifying Cannabis accessions, making it a valuable tool for future taxonomic studies, cultivar recognition, and possibly chemical content analysis and sex identification, in addition to permitting the morphometric analysis of leaves in any species with variable numbers of leaflets or lobes.

Enhanced genome-wide association reveals the role of YABBY11-NGATHA-LIKE1 in leaf serration development of Populus

Leaf margins are complex plant morphological features that contribute to leaf shape diversity, which affects plant structure, yield, and adaptation. Although several leaf margin regulators have been identified to date, the genetic basis of their natural variation has not been fully elucidated. In this study, we profiled two distinct leaf morphology types (serrated and smooth) using the persistent homology mathematical framework (PHMF) in two poplar species (Populus tomentosa and Populus simonii, respectively). A combined genome-wide association study (GWAS) and expression quantitative trait nucleotide (eQTN) mapping were applied to create a leaf morphology control module using data from P. tomentosa and P. simonii populations. Natural variation in leaf margins was associated with YABBY11 (YAB11) transcript abundance in poplar. In P. tomentosa, PtoYAB11 carries a premature stop codon (PtoYAB11PSC), resulting in the loss of its positive regulation of NGATHA-LIKE1 (PtoNGAL-1) and RIBULOSE BISPHOSPHATE CARBOXYLASE LARGE SUBUNIT (PtoRBCL). Overexpression of PtoYAB11PSC promoted serrated leaf margins, enlarged leaves, enhanced photosynthesis, and increased biomass. Overexpression of PsiYAB11 in P. tomentosa promoted smooth leaf margins, higher stomatal density, and greater light damage repair ability. In poplar, YAB11-NGAL1 is sensitive to environmental conditions, acts as a positive regulator of leaf margin serration, and may also link environmental signaling to leaf morphological plasticity.

Linking Genes to Shape in Plants Using Morphometrics

A transition from qualitative to quantitative descriptors of morphology has been facilitated through the growing field of morphometrics, representing the conversion of shapes and patterns into numbers. The analysis of plant form at the macromorphological scale using morphometric approaches quantifies what is commonly referred to as a phenotype. Quantitative phenotypic analysis of individuals with contrasting genotypes in turn provides a means to establish links between genes and shapes. The path from a gene to a morphological phenotype is, however, not direct, with instructive information progressing both across multiple scales of biological complexity and through nonintuitive feedback, such as mechanical signals. In this review, we explore morphometric approaches used to perform whole-plant phenotyping and quantitative approaches in capture processes in the mesoscales, which bridge the gaps between genes and shapes in plants. Quantitative frameworks involving both the computational simulation and the discretization of data into networks provide a putative path to predicting emergent shape from underlying genetic programs.

Contour analysis for interpretable leaf shape category discovery

BACKGROUND

The categorical description of leaf shapes is of paramount importance in ecology, taxonomy and paleobotanical studies. Classification systems proposed by domain experts support these descriptions. Despite the importance of these visual descriptive systems, classifications based on this expert's knowledge may be ambiguous or limited when representing shapes in unknown scenarios, as expected for biological exploratory domains. This work proposes a novel strategy to automatically discover the shape categories in a set of unlabeled leaves by only using the leaf-shape information. In particular, we overcome the task of discovering shape categories from different plant species for three different biological settings.

RESULTS

The proposed method may successfully infer the unknown underlying shape categories with an F-score greater than 92%.

CONCLUSIONS

The approach also provided high levels of visual interpretability, an essential requirement in the description of biological objects. This method may support morphological analysis of biological objects in exploratory domains.