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

Stomata-Photosynthesis Synergy Mediates Combined Heat and Salt Stress Tolerance in Sugarcane Mutant M4209

Sugarcane (Saccharum officinarum L.) is an economically important long-duration crop which is currently facing concurrent heat waves and soil salinity. The present study evaluates an inducible salt-tolerant sugarcane mutant M4209, developed via radiation-induced mutagenesis of elite check variety Co 86032, under heat (42/30°C; day/night), NaCl (200 mM) or heat + NaCl (HS)-stress conditions. Though heat application significantly improved plant growth and biomass in both genotypes, this beneficial impact was partially diminished in Co 86032 under HS-stress conditions, coinciding with higher Na+ accumulation and lower triacylglycerol levels. Besides, heat broadly equalised the negative impact on NaCl stress in terms of various physiological and biochemical attributes in both the genotypes, indicating its spaciotemporal advantage. The simultaneous up- and downregulation of antagonistic regulators, epidermal patterning factor (EPF) 9 (SoEPF9) and SoEPF2, respectively attributed to the OSD (Open Small Dense) stomatal phenotype in M4209, which resulted into enhanced conductance, transpirational cooling and gaseous influx. This led to improved photoassimilation, which was supported by higher plastidic:nonplastidic lipid ratio, upregulation of SoRCA (Rubisco activase) and better source strength, resulting in overall plant growth enhancement across all the tested stress scenarios. Taken together, the present study emphasised the knowledge-driven harnessing of stomatal-photosynthetic synergy for ensuring global sugarcane productivity, especially under "salt-heat" coupled stress scenarios.

Interannual Variation of Stomatal Traits Impacts the Environmental Responses of Apple Trees

Stomata are fundamental to plant-water relations and represent promising targets to enhance crop water-use efficiency and climate resilience. Here, we investigated stomatal density (SD) variation in 269 apple accessions across 3 years (2019-2021), which demonstrated significant differences between accessions but consistency over time. We selected 2 subsets of 20 accessions, each with contrasting SD: high stomatal density (HSD; 370-500 mm-2) and low stomatal density (LSD; 192-316 mm-2). SD groups were compared in stomatal function, leaf physiology and crop productivity across two seasons (2021-2022). LSD had lower stomatal conductance (gs) and higher intrinsic water-use efficiency in both years (p < 0.05). Hotter and drier conditions in 2022 reduced gs similarly in both groups (-22% HSD, -21% LSD), but also created a difference in net carbon assimilation (Anet) that was not present in 2021 (HSD + 1.7 μmol CO2 m-2 s-1, p < 0.05). LSD constraints on Anet were reflected in carbon isotope discrimination (δ13C, p < 0.001) and annual decline in fruit yield (-35%, p < 0.001). Our results demonstrate the suitability of SD as a trait to improve WUE, but also identifies a trade-off between water savings and productivity, which requires consideration for breeding.

Analysis of stomatal characteristics of maize hybrids and their parental inbred lines during critical reproductive periods

The stomatal phenotype is a crucial microscopic characteristic of the leaf surface, and modulating the stomata of maize leaves can enhance photosynthetic carbon assimilation and water use efficiency, thereby playing a vital role in maize yield formation. The evolving imaging and image processing technologies offer effective tools for precise analysis of stomatal phenotypes. This study employed Jingnongke 728 and its parental inbred to capture stomatal images from various leaf positions and abaxial surfaces during key reproductive stages using rapid scanning electron microscopy. We uesd a target detection and image segmentation approach based on YOLOv5s and Unet to efficiently obtain 11 phenotypic traits encompassing stomatal count, shape, and distribution. Manual validation revealed high detection accuracies for stomatal density, width, and length, with R2 values of 0.92, 0.97, and 0.95, respectively. Phenotypic analyses indicated a significant positive correlation between stomatal density and the percentage of guard cells and pore area (r=0.36), and a negative correlation with stomatal area and subsidiary cell area (r=-0.34 and -0.46). Additionally, stomatal traits exhibited notable variations with reproductive stages and leaf layers. Specifically, at the monocot scale, stomatal density increased from 74.35 to 87.19 Counts/mm2 from lower to upper leaf layers. Concurrently, the stomatal shape shifted from sub-circular (stomatal roundness = 0.64) to narrow and elongated (stomatal roundness = 0.63). Throughout the growth cycle, stomatal density remained stable during vegetative growth, decreased during reproductive growth with smaller size and narrower shape, and continued to decline while increasing in size and tending towards a rounded shape during senescence. Remarkably, hybrid 728 differed notably from its parents in stomatal phenotype, particularly during senescence. Moreover, the stomatal density of the hybrids showed negative super parental heterosis (heterosis rate = -0.09), whereas stomatal dimensions exhibited positive super parental heterosis, generally resembling the parent MC01. This investigation unveils the dynamic variations in maize stomatal phenotypes, bolstering genetic analyses and targeted improvements in maize, and presenting a novel technological instrument for plant phenotype studies.

Comparative analysis of stomatal pore instance segmentation: Mask R-CNN vs. YOLOv8 on Phenomics Stomatal dataset

This study conducts a rigorous comparative analysis between two cutting-edge instance segmentation methods, Mask R-CNN and YOLOv8, focusing on stomata pore analysis. A novel dataset specifically tailored for stomata pore instance segmentation, named PhenomicsStomata, was introduced. This dataset posed challenges such as low resolution and image imperfections, prompting the application of advanced preprocessing techniques, including image enhancement using the Lucy-Richardson Algorithm. The models underwent comprehensive evaluation, considering accuracy, precision, and recall as key parameters. Notably, YOLOv8 demonstrated superior performance over Mask R-CNN, particularly in accurately calculating stomata pore dimensions. Beyond this comparative study, the implications of our findings extend across diverse biological research, providing a robust foundation for advancing our understanding of plant physiology. Furthermore, the preprocessing enhancements offer valuable insights for refining image analysis techniques, showcasing the potential for broader applications in scientific domains. This research marks a significant stride in unraveling the complexities of plant structures, offering both theoretical insights and practical applications in scientific research.

Machine learning-enabled computer vision for plant phenotyping: a primer on AI/ML and a case study on stomatal patterning

Artificial intelligence and machine learning (AI/ML) can be used to automatically analyze large image datasets. One valuable application of this approach is estimation of plant trait data contained within images. Here we review 39 papers that describe the development and/or application of such models for estimation of stomatal traits from epidermal micrographs. In doing so, we hope to provide plant biologists with a foundational understanding of AI/ML and summarize the current capabilities and limitations of published tools. While most models show human-level performance for stomatal density (SD) quantification at superhuman speed, they are often likely to be limited in how broadly they can be applied across phenotypic diversity associated with genetic, environmental, or developmental variation. Other models can make predictions across greater phenotypic diversity and/or additional stomatal/epidermal traits, but require significantly greater time investment to generate ground-truth data. We discuss the challenges and opportunities presented by AI/ML-enabled computer vision analysis, and make recommendations for future work to advance accelerated stomatal phenotyping.

Application of deep learning for the analysis of stomata: a review of current methods and future directions

Plant physiology and metabolism rely on the function of stomata, structures on the surface of above-ground organs that facilitate the exchange of gases with the atmosphere. The morphology of the guard cells and corresponding pore that make up the stomata, as well as the density (number per unit area), are critical in determining overall gas exchange capacity. These characteristics can be quantified visually from images captured using microscopy, traditionally relying on time-consuming manual analysis. However, deep learning (DL) models provide a promising route to increase the throughput and accuracy of plant phenotyping tasks, including stomatal analysis. Here we review the published literature on the application of DL for stomatal analysis. We discuss the variation in pipelines used, from data acquisition, pre-processing, DL architecture, and output evaluation to post-processing. We introduce the most common network structures, the plant species that have been studied, and the measurements that have been performed. Through this review, we hope to promote the use of DL methods for plant phenotyping tasks and highlight future requirements to optimize uptake, predominantly focusing on the sharing of datasets and generalization of models as well as the caveats associated with utilizing image data to infer physiological function.

Stomatal development in the changing climate

Stomata, microscopic pores flanked by symmetrical guard cells, are vital regulators of gas exchange that link plant processes with environmental dynamics. The formation of stomata involves the multi-step progression of a specialized cell lineage. Remarkably, this process is heavily influenced by environmental factors, allowing plants to adjust stomatal production to local conditions. With global warming set to alter our climate at an unprecedented pace, understanding how environmental factors impact stomatal development and plant fitness is becoming increasingly important. In this Review, we focus on the effects of carbon dioxide, high temperature and drought - three environmental factors tightly linked to global warming - on stomatal development. We summarize the stomatal response of a variety of plant species and highlight the existence of species-specific adaptations. Using the model plant Arabidopsis, we also provide an update on the molecular mechanisms involved in mediating the plasticity of stomatal development. Finally, we explore how knowledge on stomatal development is being applied to generate crop varieties with optimized stomatal traits that enhance their resilience against climate change and maintain agricultural productivity.

Genetic Analysis of Soybean Flower Size Phenotypes Based on Computer Vision and Genome-Wide Association Studies

The dimensions of organs such as flowers, leaves, and seeds are governed by processes of cellular proliferation and expansion. In soybeans, the dimensions of these organs exhibit a strong correlation with crop yield, quality, and other phenotypic traits. Nevertheless, there exists a scarcity of research concerning the regulatory genes influencing flower size, particularly within the soybean species. In this study, 309 samples of 3 soybean types (123 cultivar, 90 landrace, and 96 wild) were re-sequenced. The microscopic phenotype of soybean flower organs was photographed using a three-eye microscope, and the phenotypic data were extracted by means of computer vision. Pearson correlation analysis was employed to assess the relationship between petal and seed phenotypes, revealing a strong correlation between the sizes of these two organs. Through GWASs, SNP loci significantly associated with flower organ size were identified. Subsequently, haplotype analysis was conducted to screen for upstream and downstream genes of these loci, thereby identifying potential candidate genes. In total, 77 significant SNPs associated with vexil petals, 562 significant SNPs associated with wing petals, and 34 significant SNPs associated with keel petals were found. Candidate genes were screened by candidate sites, and haplotype analysis was performed on the candidate genes. Finally, the present investigation yielded 25 and 10 genes of notable significance through haplotype analysis in the vexil and wing regions, respectively. Notably, Glyma.07G234200, previously documented for its high expression across various plant organs, including flowers, pods, leaves, roots, and seeds, was among these identified genes. The research contributes novel insights to soybean breeding endeavors, particularly in the exploration of genes governing organ development, the selection of field materials, and the enhancement of crop yield. It played a role in the process of material selection during the growth period and further accelerated the process of soybean breeding material selection.

Investigation of a Perspective Urban Tree Species, Ginkgo biloba L., by Scientific Analysis of Historical Old Specimens

In this study, we examined over 200-year-old Ginkgo biloba L. specimens under different environmental conditions. The overall aim was to explore which factors influence their vitality and general fitness in urban environments and thus their ability to tolerate stressful habitats. In order to determine this, we used a number of different methods, including histological examinations (stomatal density and size) and physiological measurements (peroxidase enzyme activity), as well as assessing the air pollution tolerance index (APTI). The investigation of the genetic relationships between individuals was performed using flow cytometry and miRNA marker methods. The genetic tests revealed that all individuals are diploid, whereas the lus-miR168 and lus-miR408 markers indicated a kinship relation between them. These results show that the effect of different habitat characteristics can be detected through morphological and physiological responses, thus indicating relatively higher stress values for all studied individuals. A significant correlation can be found between the level of adaptability and the relatedness of the examined individuals. These results suggest that Ginkgo biloba L. is well adapted to an environment with increased stress factors and therefore suitable for use in urban areas.

Deep learning-based quantification and transcriptomic profiling reveal a methyl jasmonate-mediated glandular trichome formation pathway in Cannabis sativa

Cannabis glandular trichomes (GTs) are economically and biotechnologically important structures that have a remarkable morphology and capacity to produce, store, and secrete diverse classes of secondary metabolites. However, our understanding of the developmental changes and the underlying molecular processes involved in cannabis GT development is limited. In this study, we developed Cannabis Glandular Trichome Detection Model (CGTDM), a deep learning-based model capable of differentiating and quantifying three types of cannabis GTs with a high degree of efficiency and accuracy. By profiling at eight different time points, we captured dynamic changes in gene expression, phenotypes, and metabolic processes associated with GT development. By integrating weighted gene co-expression network analysis with CGTDM measurements, we established correlations between phenotypic variations in GT traits and the global transcriptome profiles across the developmental gradient. Notably, we identified a module containing methyl jasmonate (MeJA)-responsive genes that significantly correlated with stalked GT density and cannabinoid content during development, suggesting the existence of a MeJA-mediated GT formation pathway. Our findings were further supported by the successful promotion of GT development in cannabis through exogenous MeJA treatment. Importantly, we have identified CsMYC4 as a key transcription factor that positively regulates GT formation via MeJA signaling in cannabis. These findings provide novel tools for GT detection and counting, as well as valuable information for understanding the molecular regulatory mechanism of GT formation, which has the potential to facilitate the molecular breeding, targeted engineering, informed harvest timing, and manipulation of cannabinoid production.

Measuring stomatal and guard cell metrics for plant physiology and growth using StoManager1

Automated guard cell detection and measurement are vital for understanding plant physiological performance and ecological functioning in global water and carbon cycles. Most current methods for measuring guard cells and stomata are laborious, time-consuming, prone to bias, and limited in scale. We developed StoManager1, a high-throughput tool utilizing geometrical, mathematical algorithms, and convolutional neural networks to automatically detect, count, and measure over 30 guard cell and stomatal metrics, including guard cell and stomatal area, length, width, stomatal aperture area/guard cell area, orientation, stomatal evenness, divergence, and aggregation index. Combined with leaf functional traits, some of these StoManager1-measured guard cell and stomatal metrics explained 90% and 82% of tree biomass and intrinsic water use efficiency (iWUE) variances in hardwoods, making them substantial factors in leaf physiology and tree growth. StoManager1 demonstrated exceptional precision and recall (mAP@0.5 over 0.96), effectively capturing diverse stomatal properties across over 100 species. StoManager1 facilitates the automation of measuring leaf stomatal and guard cells, enabling broader exploration of stomatal control in plant growth and adaptation to environmental stress and climate change. This has implications for global gross primary productivity (GPP) modeling and estimation, as integrating stomatal metrics can enhance predictions of plant growth and resource usage worldwide. Easily accessible open-source code and standalone Windows executable applications are available on a GitHub repository (https://github.com/JiaxinWang123/StoManager1) and Zenodo (https://doi.org/10.5281/zenodo.7686022).

Plant microphenotype: from innovative imaging to computational analysis

The microphenotype plays a key role in bridging the gap between the genotype and the complex macro phenotype. In this article, we review the advances in data acquisition and the intelligent analysis of plant microphenotyping and present applications of microphenotyping in plant science over the past two decades. We then point out several challenges in this field and suggest that cross-scale image acquisition strategies, powerful artificial intelligence algorithms, advanced genetic analysis, and computational phenotyping need to be established and performed to better understand interactions among genotype, environment, and management. Microphenotyping has entered the era of Microphenotyping 3.0 and will largely advance functional genomics and plant science.

Stomatal improvement for crop stress resistance

The growth and yield of crop plants are threatened by environmental challenges such as water deficit, soil flooding, high salinity, and extreme temperatures, which are becoming increasingly severe under climate change. Stomata contribute greatly to plant adaptation to stressful environments by governing transpirational water loss and photosynthetic gas exchange. Increasing evidence has revealed that stomata formation is shaped by transcription factors, signaling peptides, and protein kinases, which could be exploited to improve crop stress resistance. The past decades have seen unprecedented progress in our understanding of stomata formation, but most of these advances have come from research on model plants. This review highlights recent research in stomata formation in crops and its multifaceted functions in abiotic stress tolerance. Current strategies, limitations, and future directions for harnessing stomatal development to improve crop stress resistance are discussed.

Pilot-scale genome-wide association mapping in diverse sorghum germplasms identified novel genetic loci linked to major agronomic, root and stomatal traits

This genome-wide association studies (GWAS) used a subset of 96 diverse sorghum accessions, constructed from a large collection of 219 accessions for mining novel genetic loci linked to major agronomic, root morphological and physiological traits. The subset yielded 43,452 high quality single nucleotide polymorphic (SNP) markers exhibiting high allelic diversity. Population stratification showed distinct separation between caudatum and durra races. Linkage disequilibrium (LD) decay was rapidly declining with increasing physical distance across all chromosomes. The initial 50% LD decay was ~ 5 Kb and background level was within ~ 80 Kb. This study detected 42 significant quantitative trait nucleotide (QTNs) for different traits evaluated using FarmCPU, SUPER and 3VmrMLM which were in proximity with candidate genes related and were co-localized in already reported quantitative trait loci (QTL) and phenotypic variance (R2) of these QTNs ranged from 3 to 20%. Haplotype validation of the candidate genes from this study resulted nine genes showing significant phenotypic difference between different haplotypes. Three novel candidate genes associated with agronomic traits were validated including Sobic.001G499000, a potassium channel tetramerization domain protein for plant height, Sobic.010G186600, a nucleoporin-related gene for dry biomass, and Sobic.002G022600 encoding AP2-like ethylene-responsive transcription factor for plant yield. Several other candidate genes were validated and associated with different root and physiological traits including Sobic.005G104100, peroxidase 13-related gene with root length, Sobic.010G043300, homologous to Traes_5BL_8D494D60C, encoding inhibitor of apoptosis with iWUE, and Sobic.010G125500, encoding zinc finger, C3HC4 type domain with Abaxial stomatal density. In this study, 3VmrMLM was more powerful than FarmCPU and SUPER for detecting QTNs and having more breeding value indicating its reliable output for validation. This study justified that the constructed subset of diverse sorghums can be used as a panel for mapping other key traits to accelerate molecular breeding in sorghum.

Natural polymorphisms in ZmIRX15A affect water-use efficiency by modulating stomatal density in maize

Stomatal density (SD) is closely related to crop drought resistance. Understanding the genetic basis for natural variation in SD may facilitate efforts to improve water-use efficiency. Here, we report a genome-wide association study for SD in maize seedlings, which identified 18 genetic variants that could be resolved to seven candidate genes. A 3-bp insertion variant (InDel1089) in the last exon of Zea mays (Zm) IRX15A (Irregular xylem 15A) had the most significant association with SD and modulated the translation of ZmIRX15A mRNA by affecting its secondary structure. Dysfunction of ZmIRX15A increased SD, leading to an increase in the transpiration rate and CO2 assimilation efficiency. ZmIRX15A encodes a xylan deposition enzyme and its disruption significantly decreased xylan abundance in secondary cell wall composition. Transcriptome analysis revealed a substantial alteration of the expression of genes involved in stomatal complex morphogenesis and drought response in the loss-of-function of ZmIRX15A mutant. Overall, our study provides important genetic insights into the natural variation of leaf SD in maize, and the identified loci or genes can serve as direct targets for enhancing drought resistance in molecular-assisted maize breeding.

Data-driven approaches to improve water-use efficiency and drought resistance in crop plants

With the increasing population, there lies a pressing demand for food, feed and fibre, while the changing climatic conditions pose severe challenges for agricultural production worldwide. Water is the lifeline for crop production; thus, enhancing crop water-use efficiency (WUE) and improving drought resistance in crop varieties are crucial for overcoming these challenges. Genetically-driven improvements in yield, WUE and drought tolerance traits can buffer the worst effects of climate change on crop production in dry areas. While traditional crop breeding approaches have delivered impressive results in increasing yield, the methods remain time-consuming and are often limited by the existing allelic variation present in the germplasm. Significant advances in breeding and high-throughput omics technologies in parallel with smart agriculture practices have created avenues to dramatically speed up the process of trait improvement by leveraging the vast volumes of genomic and phenotypic data. For example, individual genome and pan-genome assemblies, along with transcriptomic, metabolomic and proteomic data from germplasm collections, characterised at phenotypic levels, could be utilised to identify marker-trait associations and superior haplotypes for crop genetic improvement. In addition, these omics approaches enable the identification of genes involved in pathways leading to the expression of a trait, thereby providing an understanding of the genetic, physiological and biochemical basis of trait variation. These data-driven gene discoveries and validation approaches are essential for crop improvement pipelines, including genomic breeding, speed breeding and gene editing. Herein, we provide an overview of prospects presented using big data-driven approaches (including artificial intelligence and machine learning) to harness new genetic gains for breeding programs and develop drought-tolerant crop varieties with favourable WUE and high-yield potential traits.

Automated estimation of stomatal number and aperture in haskap (Lonicera caerulea L.)

MAIN CONCLUSION

This study developed the reliable Mask R-CNN model to detect stomata in Lonicera caerulea. The obtained data could be utilized for evaluating some characters such as stomatal number and aperture area. The native distribution of haskap (Lonicera caerulea L.), a small-shrub species, extends through Northern Eurasia, Japan, and North America. Stomatal observation is important for plant research to evaluate the physiological status and to investigate the effect of ploidy levels on phenotypes. However, manual annotation of stomata using microscope software or ImageJ is time consuming. Therefore, an efficient method to phenotype stomata is needed. In this study, we used the Mask Regional Convolutional Neural Network (Mask R-CNN), a deep learning model, to analyze the stomata of haskap efficiently and accurately. We analyzed haskap plants (dwarf and giant phenotypes) with the same ploidy but different phenotypes, including leaf area, stomatal aperture area, stomatal density, and total number of stomata. The R-square value of the estimated stomatal aperture area was 0.92 and 0.93 for the dwarf and giant plants, respectively. The R-square value of the estimated stomatal number was 0.99 and 0.98 for the two phenotypes. The results showed that the measurements obtained using the models were as accurate as the manual measurements. Statistical analysis revealed that the stomatal density of the dwarf plants was higher than that of the giant plants, but the maximum stomatal aperture area, average stomatal aperture area, total number of stomata, and average leaf area were lower than those of the giant plants. A high-precision, rapid, and large-scale detection method was developed by training the Mask R-CNN model. This model can help save time and increase the volume of data.

StomaAI: an efficient and user-friendly tool for measurement of stomatal pores and density using deep computer vision

Using microscopy to investigate stomatal behaviour is common in plant physiology research. Manual inspection and measurement of stomatal pore features is low throughput, relies upon expert knowledge to record stomatal features accurately, requires significant researcher time and investment, and can represent a significant bottleneck to research pipelines. To alleviate this, we introduce StomaAI (SAI): a reliable, user-friendly and adaptable tool for stomatal pore and density measurements via the application of deep computer vision, which has been initially calibrated and deployed for the model plant Arabidopsis (dicot) and the crop plant barley (monocot grass). SAI is capable of producing measurements consistent with human experts and successfully reproduced conclusions of published datasets. SAI boosts the number of images that can be evaluated in a fraction of the time, so can obtain a more accurate representation of stomatal traits than is routine through manual measurement. An online demonstration of SAI is hosted at https://sai.aiml.team, and the full local application is publicly available for free on GitHub through https://github.com/xdynames/sai-app.

Genome-wide association study revealed TaHXK3-2A as a candidate gene controlling stomatal index in wheat seedlings

Stomata are important channels for the control of gas exchange between plants and the atmosphere. To examine the genetic architecture of wheat stomatal index, we performed a genome-wide association study (GWAS) using a panel of 539 wheat accessions and 450 678 polymorphic single nucleotide polymorphisms (SNPs) that were detected using wheat-specific 660K SNP array. A total of 130 SNPs were detected to be significantly associated with stomatal index in both leaf surfaces of wheat seedlings. These significant SNPs were distributed across 16 chromosomes and involved 2625 candidate genes which participate in stress response, metabolism and cell/organ development. Subsequent bulk segregant analysis (BSA), combined with GWAS identified one major haplotype on chromosome 2A, that is responsible for stomatal index on the abaxial leaf surface. Candidate gene association analysis revealed that genetic variation in the promoter region of the hexokinase gene TaHXK3-2A was significantly associated with the stomatal index. Moreover, transgenic analysis confirmed that TaHXK3-2A overexpression in wheat decreased the size of leaf pavement cells but increased stomatal density through the glucose metabolic pathway, resulting in drought sensitivity among TaHXK3-2A transgenic lines due to an increased transpiration rate. Taken together, these results provide valuable insights into the genetic control of the stomatal index in wheat seedlings.

Knock-down of phosphoenolpyruvate carboxylase 3 negatively impacts growth, productivity, and responses to salt stress in sorghum (Sorghum bicolor L.)

Phosphoenolpyruvate carboxylase (PEPC) is a carboxylating enzyme with important roles in plant metabolism. Most studies in C₄ plants have focused on photosynthetic PEPC, but less is known about non-photosynthetic PEPC isozymes, especially with respect to their physiological functions. In this work, we analyzed the precise roles of the sorghum (Sorghum bicolor) PPC3 isozyme by the use of knock-down lines with the SbPPC3 gene silenced (Ppc3 lines). Ppc3 plants showed reduced stomatal conductance and plant size, a delay in flowering time, and reduced seed production. In addition, silenced plants accumulated stress indicators such as Asn, citrate, malate, and sucrose in roots and showed higher citrate synthase activity, even in control conditions. Salinity further affected stomatal conductance and yield and had a deeper impact on central metabolism in silenced plants compared to wild type, more notably in roots, with Ppc3 plants showing higher nitrate reductase and NADH-glutamate synthase activity in roots and the accumulation of molecules with a higher N/C ratio. Taken together, our results show that although SbPPC3 is predominantly a root protein, its absence causes deep changes in plant physiology and metabolism in roots and leaves, negatively affecting maximal stomatal opening, growth, productivity, and stress responses in sorghum plants. The consequences of SbPPC3 silencing suggest that this protein, and maybe orthologs in other plants, could be an important target to improve plant growth, productivity, and resistance to salt stress and other stresses where non-photosynthetic PEPCs may be implicated.

Deep learning based high-throughput phenotyping of chalkiness in rice exposed to high night temperature

BACKGROUND

Rice is a major staple food crop for more than half the world's population. As the global population is expected to reach 9.7 billion by 2050, increasing the production of high-quality rice is needed to meet the anticipated increased demand. However, global environmental changes, especially increasing temperatures, can affect grain yield and quality. Heat stress is one of the major causes of an increased proportion of chalkiness in rice, which compromises quality and reduces the market value. Researchers have identified 140 quantitative trait loci linked to chalkiness mapped across 12 chromosomes of the rice genome. However, the available genetic information acquired by employing advances in genetics has not been adequately exploited due to a lack of a reliable, rapid and high-throughput phenotyping tool to capture chalkiness. To derive extensive benefit from the genetic progress achieved, tools that facilitate high-throughput phenotyping of rice chalkiness are needed.

RESULTS

We use a fully automated approach based on convolutional neural networks (CNNs) and Gradient-weighted Class Activation Mapping (Grad-CAM) to detect chalkiness in rice grain images. Specifically, we train a CNN model to distinguish between chalky and non-chalky grains and subsequently use Grad-CAM to identify the area of a grain that is indicative of the chalky class. The area identified by the Grad-CAM approach takes the form of a smooth heatmap that can be used to quantify the degree of chalkiness. Experimental results on both polished and unpolished rice grains using standard instance classification and segmentation metrics have shown that Grad-CAM can accurately identify chalky grains and detect the chalkiness area.

CONCLUSIONS

We have successfully demonstrated the application of a Grad-CAM based tool to accurately capture high night temperature induced chalkiness in rice. The models trained will be made publicly available. They are easy-to-use, scalable and can be readily incorporated into ongoing rice breeding programs, without rice researchers requiring computer science or machine learning expertise.

Machine learning-enabled phenotyping for GWAS and TWAS of WUE traits in 869 field-grown sorghum accessions

Sorghum (Sorghum bicolor) is a model C4 crop made experimentally tractable by extensive genomic and genetic resources. Biomass sorghum is studied as a feedstock for biofuel and forage. Mechanistic modeling suggests that reducing stomatal conductance (gs) could improve sorghum intrinsic water use efficiency (iWUE) and biomass production. Phenotyping to discover genotype-to-phenotype associations remains a bottleneck in understanding the mechanistic basis for natural variation in gs and iWUE. This study addressed multiple methodological limitations. Optical tomography and a machine learning tool were combined to measure stomatal density (SD). This was combined with rapid measurements of leaf photosynthetic gas exchange and specific leaf area (SLA). These traits were the subject of genome-wide association study and transcriptome-wide association study across 869 field-grown biomass sorghum accessions. The ratio of intracellular to ambient CO2 was genetically correlated with SD, SLA, gs, and biomass production. Plasticity in SD and SLA was interrelated with each other and with productivity across wet and dry growing seasons. Moderate-to-high heritability of traits studied across the large mapping population validated associations between DNA sequence variation or RNA transcript abundance and trait variation. A total of 394 unique genes underpinning variation in WUE-related traits are described with higher confidence because they were identified in multiple independent tests. This list was enriched in genes whose Arabidopsis (Arabidopsis thaliana) putative orthologs have functions related to stomatal or leaf development and leaf gas exchange, as well as genes with nonsynonymous/missense variants. These advances in methodology and knowledge will facilitate improving C4 crop WUE.

Machine learning-enabled phenotyping for GWAS and TWAS of WUE traits in 869 field-grown sorghum accessions

Sorghum (Sorghum bicolor) is a model C4 crop made experimentally tractable by extensive genomic and genetic resources. Biomass sorghum is studied as a feedstock for biofuel and forage. Mechanistic modeling suggests that reducing stomatal conductance (gs) could improve sorghum intrinsic water use efficiency (iWUE) and biomass production. Phenotyping to discover genotype-to-phenotype associations remains a bottleneck in understanding the mechanistic basis for natural variation in gs and iWUE. This study addressed multiple methodological limitations. Optical tomography and a machine learning tool were combined to measure stomatal density (SD). This was combined with rapid measurements of leaf photosynthetic gas exchange and specific leaf area (SLA). These traits were the subject of genome-wide association study and transcriptome-wide association study across 869 field-grown biomass sorghum accessions. The ratio of intracellular to ambient CO2 was genetically correlated with SD, SLA, gs, and biomass production. Plasticity in SD and SLA was interrelated with each other and with productivity across wet and dry growing seasons. Moderate-to-high heritability of traits studied across the large mapping population validated associations between DNA sequence variation or RNA transcript abundance and trait variation. A total of 394 unique genes underpinning variation in WUE-related traits are described with higher confidence because they were identified in multiple independent tests. This list was enriched in genes whose Arabidopsis (Arabidopsis thaliana) putative orthologs have functions related to stomatal or leaf development and leaf gas exchange, as well as genes with nonsynonymous/missense variants. These advances in methodology and knowledge will facilitate improving C4 crop WUE.

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

The stomatal index of the leaf is the ratio of the number of stomata to the total number of stomata and epidermal cells. Comparing with the stomatal density, the stomatal index is relatively constant in environmental conditions and the age of the leaf and, therefore, of diagnostic characteristics for a given genotype or species. Traditional assessment methods involve manual counting of the number of stomata and epidermal cells in microphotographs, which is labor-intensive and time-consuming. Although several automatic measurement algorithms of stomatal density have been proposed, no stomatal index pipelines are currently available. The main aim of this research is to develop an automated stomatal index measurement pipeline. The proposed method employed Faster regions with convolutional neural networks (R-CNN) and U-Net and image-processing techniques to count stomata and epidermal cells, and subsequently calculate the stomatal index. To improve the labeling speed, a semi-automatic strategy was employed for epidermal cell annotation in each micrograph. Benchmarking the pipeline on 1,000 microscopic images of leaf epidermis in the wheat dataset (Triticum aestivum L.), the average counting accuracies of 98.03 and 95.03% for stomata and epidermal cells, respectively, and the final measurement accuracy of the stomatal index of 95.35% was achieved. R 2 values between automatic and manual measurement of stomata, epidermal cells, and stomatal index were 0.995, 0.983, and 0.895, respectively. The average running time (ART) for the entire pipeline could be as short as 0.32 s per microphotograph. The proposed pipeline also achieved a good transferability on the other families of the plant using transfer learning, with the mean counting accuracies of 94.36 and 91.13% for stomata and epidermal cells and the stomatal index accuracy of 89.38% in seven families of the plant. The pipeline is an automatic, rapid, and accurate tool for the stomatal index measurement, enabling high-throughput phenotyping, and facilitating further understanding of the stomatal and epidermal development for the plant physiology community. To the best of our knowledge, this is the first deep learning-based microphotograph analysis pipeline for stomatal index assessment.