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

Elevated CO2 alters soybean physiology and defense responses, and has disparate effects on susceptibility to diverse microbial pathogens

Increasing atmospheric CO2 levels have a variety of effects that can influence plant responses to microbial pathogens. However, these responses are varied, and it is challenging to predict how elevated CO2 (eCO2) will affect a particular plant-pathogen interaction. We investigated how eCO2 may influence disease development and responses to diverse pathogens in the major oilseed crop, soybean. Soybean plants grown in ambient CO2 (aCO2, 419 parts per million (ppm)) or in eCO2 (550 ppm) were challenged with bacterial, viral, fungal, and oomycete pathogens. Disease severity, pathogen growth, gene expression, and molecular plant defense responses were quantified. In eCO2, plants were less susceptible to Pseudomonas syringae pv. glycinea (Psg) but more susceptible to bean pod mottle virus, soybean mosaic virus, and Fusarium virguliforme. Susceptibility to Pythium sylvaticum was unchanged, although a greater loss in biomass occurred in eCO2. Reduced susceptibility to Psg was associated with enhanced defense responses. Increased susceptibility to the viruses was associated with reduced expression of antiviral defenses. This work provides a foundation for understanding how future eCO2 levels may impact molecular responses to pathogen challenges in soybean and demonstrates that microbes infecting both shoots and roots are of potential concern in future climatic conditions.

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.

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.

Drought priming at seedling stage improves photosynthetic performance and yield of potato exposed to a short-term drought stress

Potato (Solanum tuberosum L.) is an important food and vegetable crop worldwide. In recent years, the arid environment resulting from climate change has caused a sharp decline in potato yield. To clarify the effect of drought priming at the seedling stage on the tolerance of potato plants to drought stress during tuber expansion, we conducted a pot experiment to investigate the physiological response of the plants generated from seed potatoes of the variety 'Favorita' to varied water supply conditions: normal water supply at the seedling stage (control), normal water supply at the seedling stage and drought stress at the mid-tuber-expansion stage (non-primed), and drought priming at the seedling stage plus drought stress at the mid-tuber-expansion stage (primed). Drought priming resulted in an increase in the number of small vascular bundles in potato plants compared to non-primed plants. It also altered the shape and density of stomata, enhancing water use efficiency and reducing whole-plant transpiration. The primed plants maintained the basal stem cambium for a longer time under drought stress, which gained an extended differentiation ability to generate a greater number of small vascular bundles compared to non-primed plants. Drought priming increased the amount and rate of dry matter translocation, and so reduced the adverse effects on tubers of potato under drought stress. Therefore, drought priming at the seedling stage improved the photosynthetic performance and yield, and probably enhanced the drought tolerance of potato.

Drought-Alleviating Effects of Endophytic Bacteria Isolated from Xerophytic Plants on Capsicum annuum L. Seedlings

In the current study, 51 endophytic bacteria were isolated from 5 different xerophytic plants. Their drought tolerance properties were screened in vitro, and from these, four endophytes with tolerance up to - 1.5 MPa water potential were further selected and identified as Acinetobacter sp. Eo3, Pseudomonas sp. Ni5, Bacillus safensis Ni7, and Stenotrophomonas sp. C3. Due to biosafety concern, Acinetobacter sp. Eo3 and Pseudomonas sp. Ni5 were excluded from further investigation, while B. safensis Ni7 and Stenotrophomonas sp. C3 were subjected to detailed study. The drought tolerance properties of these endophytes were evaluated in vivo using Capsicum annuum L. by analysing the growth parameters (leaf number, root number, shoot length, and plant fresh weight) as well as physiological and biochemical parameters (stomatal index, relative water content, chlorophyll content, and carbohydrate accumulation) of bacteria-treated and control seedlings. Here, treatment with B. safensis Ni7 and Stenotrophomonas sp. C3 was found to result in statistically significant enhancement (P ≤ 0.001) of the measured parameters of plants when compared with the control groups. In the case of fresh weight itself, Ni7 and C3 treatment was found to result in values of 157.76 and 142.8 mg, respectively, and was statistically significant enhancement as the same for nutrient broth and distilled water control were 73.3 mg and 70.5 mg only. Additionally, the endophyte-treated seedlings displayed significant improvement in other growth parameters even under induced drought stress. These findings highlight the potential of xerophytic-derived bacterial endophytes to have significant role in mitigating the drought stress effects in plants with the promises for field application.

A Systematic Review of Effective Hardware and Software Factors Affecting High-Throughput Plant Phenotyping

Plant phenotyping studies the complex characteristics of plants, with the aim of evaluating and assessing their condition and finding better exemplars. Recently, a new branch emerged in the phenotyping field, namely, high-throughput phenotyping (HTP). Specifically, HTP exploits modern data sampling techniques to gather a high amount of data that can be used to improve the effectiveness of phenotyping. Hence, HTP combines the knowledge derived from the phenotyping domain with computer science, engineering, and data analysis techniques. In this scenario, machine learning (ML) and deep learning (DL) algorithms have been successfully integrated with noninvasive imaging techniques, playing a key role in automation, standardization, and quantitative data analysis. This study aims to systematically review two main areas of interest for HTP: hardware and software. For each of these areas, two influential factors were identified: for hardware, platforms and sensing equipment were analyzed; for software, the focus was on algorithms and new trends. The study was conducted following the PRISMA protocol, which allowed the refinement of the research on a wide selection of papers by extracting a meaningful dataset of 32 articles of interest. The analysis highlighted the diffusion of ground platforms, which were used in about 47% of reviewed methods, and RGB sensors, mainly due to their competitive costs, high compatibility, and versatility. Furthermore, DL-based algorithms accounted for the larger share (about 69%) of reviewed approaches, mainly due to their effectiveness and the focus posed by the scientific community over the last few years. Future research will focus on improving DL models to better handle hardware-generated data. The final aim is to create integrated, user-friendly, and scalable tools that can be directly deployed and used on the field to improve the overall crop yield.

Effects of high air temperature, drought, and both combinations on maize: A case study

High air temperature (HAT) and natural soil drought (NSD) have seriously affected crop yield and frequently take place in a HAT-NSD combination. Maize (Zea mays) is an important crop, thermophilic but not heat tolerant. In this study, HAT, NSD, and HAT-NSD effects on maize inbred line Huangzao4 -were characterized. Main findings were as follows: H₂O₂ and O^- accumulated much more in immature young leaves than in mature old leaves under the stresses. Lateral roots were highly distributed near the upper pot mix layers under HAT and near root tips under HAT-NSD. Saccharide accumulated mainly in stressed root caps (RC) and formed a highly accumulated saccharide band at the boundary between RC and meristematic zone. Lignin deposition was in stressed roots under NSD and HAT-NSD. Chloroplasts increased in number and formed a high-density ring around leaf vascular bundles (VB) under HAT and HAT-NSD, and sparsely scattered in the peripheral area of VBs under NSD. The RC cells containing starch granules were most under NAD-HAT but least under HAT. Under NSD and HAT-NSD followed by re-watering, anther number per tassel spikelet reduced to 3. These results provide multiple clues for further distinguishing molecular mechanisms for maize to tolerate these stresses.

JustDeepIt: Software tool with graphical and character user interfaces for deep learning-based object detection and segmentation in image analysis

Image processing and analysis based on deep learning are becoming mainstream and increasingly accessible for solving various scientific problems in diverse fields. However, it requires advanced computer programming skills and a basic familiarity with character user interfaces (CUIs). Consequently, programming beginners face a considerable technical hurdle. Because potential users of image analysis are experimentalists, who often use graphical user interfaces (GUIs) in their daily work, there is a need to develop GUI-based easy-to-use deep learning software to support their work. Here, we introduce JustDeepIt, a software written in Python, to simplify object detection and instance segmentation using deep learning. JustDeepIt provides both a GUI and a CUI. It contains various functional modules for model building and inference, and it is built upon the popular PyTorch, MMDetection, and Detectron2 libraries. The GUI is implemented using the Python library FastAPI, simplifying model building for various deep learning approaches for beginners. As practical examples of JustDeepIt, we prepared four case studies that cover critical issues in plant science: (1) wheat head detection with Faster R-CNN, YOLOv3, SSD, and RetinaNet; (2) sugar beet and weed segmentation with Mask R-CNN; (3) plant segmentation with U2-Net; and (4) leaf segmentation with U2-Net. The results support the wide applicability of JustDeepIt in plant science applications. In addition, we believe that JustDeepIt has the potential to be applied to deep learning-based image analysis in various fields beyond plant science.

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.

Cotton Breeding in Australia: Meeting the Challenges of the 21st Century

The Commonwealth Scientific and Industrial Research Organisation (CSIRO) cotton breeding program is the sole breeding effort for cotton in Australia, developing high performing cultivars for the local industry which is worth∼AU$3 billion per annum. The program is supported by Cotton Breeding Australia, a Joint Venture between CSIRO and the program's commercial partner, Cotton Seed Distributors Ltd. (CSD). While the Australian industry is the focus, CSIRO cultivars have global impact in North America, South America, and Europe. The program is unique compared with many other public and commercial breeding programs because it focuses on diverse and integrated research with commercial outcomes. It represents the full research pipeline, supporting extensive long-term fundamental molecular research; native and genetically modified (GM) trait development; germplasm enhancement focused on yield and fiber quality improvements; integration of third-party GM traits; all culminating in the release of new commercial cultivars. This review presents evidence of past breeding successes and outlines current breeding efforts, in the areas of yield and fiber quality improvement, as well as the development of germplasm that is resistant to pests, diseases and abiotic stressors. The success of the program is based on the development of superior germplasm largely through field phenotyping, together with strong commercial partnerships with CSD and Bayer CropScience. These relationships assist in having a shared focus and ensuring commercial impact is maintained, while also providing access to markets, traits, and technology. The historical successes, current foci and future requirements of the CSIRO cotton breeding program have been used to develop a framework designed to augment our breeding system for the future. This will focus on utilizing emerging technologies from the genome to phenome, as well as a panomics approach with data management and integration to develop, test and incorporate new technologies into a breeding program. In addition to streamlining the breeding pipeline for increased genetic gain, this technology will increase the speed of trait and marker identification for use in genome editing, genomic selection and molecular assisted breeding, ultimately producing novel germplasm that will meet the coming challenges of the 21st Century.

A Deep Learning Method for Fully Automatic Stomatal Morphometry and Maximal Conductance Estimation

Stomata are integral to plant performance, enabling the exchange of gases between the atmosphere and the plant. The anatomy of stomata influences conductance properties with the maximal conductance rate, g _smax, calculated from density and size. However, current calculations of stomatal dimensions are performed manually, which are time-consuming and error prone. Here, we show how automated morphometry from leaf impressions can predict a functional property: the anatomical g_smax. A deep learning network was derived to preserve stomatal morphometry via semantic segmentation. This forms part of an automated pipeline to measure stomata traits for the estimation of anatomical g_smax. The proposed pipeline achieves accuracy of 100% for the distinction (wheat vs. poplar) and detection of stomata in both datasets. The automated deep learning-based method gave estimates for g_smax within 3.8 and 1.9% of those values manually calculated from an expert for a wheat and poplar dataset, respectively. Semantic segmentation provides a rapid and repeatable method for the estimation of anatomical g_smax from microscopic images of leaf impressions. This advanced method provides a step toward reducing the bottleneck associated with plant phenotyping approaches and will provide a rapid method to assess gas fluxes in plants based on stomata morphometry.