Classical phenotyping and deep learning concur on genetic control of stomatal density and area in sorghum

Abstract

Abstract Stomatal density (SD) and stomatal complex area (SCA) are important traits that regulate gas exchange and abiotic stress response in plants. Despite sorghum (Sorghum bicolor) adaptation to arid conditions, the genetic potential of stomata-related traits remains unexplored due to challenges in available phenotyping methods. Hence, identifying loci that control stomatal traits is fundamental to designing strategies to breed sorghum with optimized stomatal regulation. We implemented both classical and deep learning methods to characterize genetic diversity in 311 grain sorghum accessions for stomatal traits at two different field environments. Nearly 12,000 images collected from abaxial (Ab) and adaxial (Ad) leaf surfaces revealed substantial variation in stomatal traits. Our study demonstrated significant accuracy between manual and deep learning methods in predicting SD and SCA. In sorghum, SD was 32%–39% greater on the Ab versus the Ad surface, while SCA on the Ab surface was 2%–5% smaller than on the Ad surface. Genome-Wide Association Study identified 71 genetic loci (38 were environment-specific) with significant genotype to phenotype associations for stomatal traits. Putative causal genes underlying the phenotypic variation were identified. Accessions with similar SCA but carrying contrasting haplotypes for SD were tested for stomatal conductance and carbon assimilation under field conditions. Our findings provide a foundation for further studies on the genetic and molecular mechanisms controlling stomata patterning and regulation in sorghum. An integrated physiological, deep learning, and genomic approach allowed us to unravel the genetic control of natural variation in stomata traits in sorghum, which can be applied to other plants.

Plant phenotyping relevance

ソルガム気孔形質の遺伝解析が主目的だが、古典的手法と深層学習による画像フェノタイピングを実装し、手動法との精度比較・検証を行っており、形質抽出法が実質的に中心的な役割を持つ。

Code and data availability

The article describes a Mask R-CNN-based stomata phenotyping pipeline and GWAS on the sorghum association panel, but no authors' public deposit of the 11,196 stomatal images, annotations, trained model, or analysis code is stated. All URLs mentioned (Kansas Mesonet weather, Sorghum QTL atlas, Mask_RCNN repository, VGG,

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