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

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 and adaxial 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 abaxial vs. the adaxial surface, while SCA on the abaxial surface was 2-5% lower than on the adaxial surface. GWAS 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 clarify the genetic control of natural variation in stomata traits in sorghum, and can be applied to other plants.

Deep learning-based high-throughput phenotyping accelerates gene discovery for stomatal traits

“…In maize, the computer-predicted means of stomatal complex density and pavement cell density showed high significant correlations with those of the manually obtained values ( R 2 SCD = 0.974 and R 2 PD = 0.961, respectively). Bheemanahalli et al (2021) showed a significant ( P < 0.001) strong relationship between predicted and manual observations of abaxial and adaxial stomata density, suggesting the reliability and accuracy of automated deep learning-based methods.…”

“…A genome-wide association study (GWAS) on diverse grain sorghum accessions by Bheemanahalli et al (2021) provided evidence for more than 71 genetic loci having significant association with stomatal traits, such as abaxial and adaxial stomatal density and stomatal complex area, and almost half as many overlapped with previously reported genomic regions. Further clarification of these regions revealed candidate putative genes including ATP-binding cassette transporter , BRASSINOSTEROID INSENSITIVE 1-associated receptor kinase 1 , homeodomain-START transcription factor , and basic helix-loop-helix family transcription factor , putative orthologs of which are known to regulate leaf development, stomatal morphology, and stomatal lineage, respectively, in dicot and monocot models.…”

“…In this issue of Plant Physiology , Bheemanahalli et al (2021) , Ferguson et al (2021) , and Xie et al (2021) introduce the use of AI-enabled high-throughput stomatal phenotyping platforms in combination with screening methods to identify specific genes and variations controlling stomatal-related traits in sorghum and maize. While considerable attempts have been made to address the bottlenecks associated with the phenotyping of stomatal traits through computer-aided image acquisition, previously developed methods suffered from issues of being time- and labor-intensive and of inaccurate stomata identification, classification, and quantification in C 4 grass species ( Furbank and Tester, 2011 ).…”

“…C, Association of phenotypic data with genetic variants. Adapted from Figure 1B in Xie et al (2021) , Figure 3A in Bheemanahalli et al (2021) , and Figure 5D in Ferguson et al (2021) .…”

A Deep Learning-Based Method for Automatic Assessment of Stomatal Index in Wheat Microscopic Images of Leaf Epidermis

“…Stomata recognition is not an exception. General one-stage object detection algorithms, single shot multiBox detector (SSD, Sakoda et al, 2019) and you only look once (YOLO, Casado-García et al, 2020), and two-stage object detection algorithm, real-time object detection with Faster R-CNN (Li et al, 2019) and mask regionbased CNN (Mask R-CNN, Bheemanahalli et al, 2021) built accurate stomata detection models for many plant species such as rice, soybean, wheat, barley, or sorghum. This study selected the Faster R-CNN for detecting and counting stomata by considering the speed-accuracy trade-off of the model.…”