Automated Quantitative Measurement of Yellow Halos Suggests Activity of Necrotrophic Effectors in Septoria tritici Blotch.

Many necrotrophic plant pathogens utilize host-selective toxins or necrotrophic effectors during the infection process. We hypothesized that the chlorotic yellow halos frequently observed around necrotic lesions caused by the wheat pathogen Zymoseptoria tritici could result from the activity of necrotrophic effectors interacting with the products of toxin sensitivity genes. As an initial step toward testing this hypothesis, we developed an automated image analysis (AIA) workflow that could quantify the degree of yellow halo formation occurring in wheat leaves naturally infected by a highly diverse pathogen population under field conditions. This AIA based on statistical learning was applied to more than 10,000 naturally infected leaves collected from 335 wheat cultivars grown in a replicated field experiment. We estimated a high heritability (h2 = 0.71) for the degree of yellow halo formation, suggesting that this quantitative trait has a significant genetic component. Using genome-wide association mapping, we identified six chromosome segments significantly associated with the yellow halo phenotype. Most of these segments contained candidate genes associated with targets of necrotrophic effectors in other necrotrophic pathogens. Our findings conform with the hypothesis that toxin sensitivity genes could account for a significant fraction of the observed variation in quantitative resistance to Septoria tritici blotch. [Formula: see text] Copyright © 2022 The Author(s). This is an open access article distributed under the CC BY 4.0 International license.

Temperature response of wheat affects final height and the timing of stem elongation under field conditions.

In wheat, temperature affects the timing and intensity of stem elongation. Genetic variation for this process is therefore important for adaptation. This study investigates the genetic response to temperature fluctuations during stem elongation and its relationship to phenology and height. Canopy height of 315 wheat genotypes (GABI wheat panel) was scanned twice weekly in the field phenotyping platform (FIP) of ETH Zurich using a LIDAR. Temperature response was modelled using linear regressions between stem elongation and mean temperature in each measurement interval. This led to a temperature-responsive (slope) and a temperature-irresponsive (intercept) component. The temperature response was highly heritable (H2=0.81) and positively related to a later start and end of stem elongation as well as final height. Genome-wide association mapping revealed three temperature-responsive and four temperature-irresponsive quantitative trait loci (QTLs). Furthermore, putative candidate genes for temperature-responsive QTLs were frequently related to the flowering pathway in Arabidopsis thaliana, whereas temperature-irresponsive QTLs corresponded to growth and reduced height genes. In combination with Rht and Ppd alleles, these loci, together with the loci for the timing of stem elongation, accounted for 71% of the variability in height. This demonstrates how high-throughput field phenotyping combined with environmental covariates can contribute to a smarter selection of climate-resilient crops.

Root Tip Shape Governs Root Elongation Rate under Increased Soil Strength.

Increased soil strength due to soil compaction or soil drying is a major limitation to root growth and crop productivity. Roots need to exert higher penetration force, resulting in increased penetration stress when elongating in soils of greater strength. This study aimed to quantify how the genotypic diversity of root tip geometry and root diameter influences root elongation under different levels of soil strength and to determine the extent to which roots adjust to increased soil strength. Fourteen wheat (Triticum aestivum) varieties were grown in soil columns packed to three bulk densities representing low, moderate, and high soil strength. Under moderate and high soil strength, smaller root tip radius-to-length ratio was correlated with higher genotypic root elongation rate, whereas root diameter was not related to genotypic root elongation. Based on cavity expansion theory, it was found that smaller root tip radius-to-length ratio reduced penetration stress, thus enabling higher root elongation rates in soils with greater strength. Furthermore, it was observed that roots could only partially adjust to increased soil strength. Root thickening was bounded by a maximum diameter, and root tips did not become more acute in response to increased soil strength. The obtained results demonstrated that root tip geometry is a pivotal trait governing root penetration stress and root elongation rate in soils of greater strength. Hence, root tip shape needs to be taken into account when selecting for crop varieties that may tolerate high soil strength.

Non-destructive measurement of soybean leaf thickness via X-ray computed tomography allows the study of diel leaf growth rhythms in the third dimension.

Present-day high-resolution leaf growth measurements provide exciting insights into diel (24-h) leaf growth rhythms and their control by the circadian clock, which match photosynthesis with oscillating environmental conditions. However, these methods are based on measurements of leaf area or elongation and neglect diel changes of leaf thickness. In contrast, the influence of various environmental stress factors to which leaves are exposed to during growth on the final leaf thickness has been studied extensively. Yet, these studies cannot elucidate how variation in leaf area and thickness are simultaneously regulated and influenced on smaller time scales. Only few methods are available to measure the thickness of young, growing leaves non-destructively. Therefore, we evaluated X-ray computed tomography to simultaneously and non-invasively record diel changes and growth of leaf thickness and area. Using conventional imaging and X-ray computed tomography leaf area, thickness and volume growth of young soybean leaves were simultaneously and non-destructively monitored at three cardinal time points during night and day for a period of 80 h under non-stressful growth conditions. Reference thickness measurements on paperboards were in good agreement to CT measurements. Comparison of CT with leaf mass data further proved the consistency of our method. Exploratory analysis showed that measurements were accurate enough for recording and analyzing relative diel changes of leaf thickness, which were considerably different to those of leaf area. Relative growth rates of leaf area were consistently positive and highest during 'nights', while diel changes in thickness fluctuated more and were temporarily negative, particularly during 'evenings'. The method is suitable for non-invasive, accurate monitoring of diel variation in leaf volume. Moreover, our results indicate that diel rhythms of leaf area and thickness show some similarity but are not tightly coupled. These differences could be due to both intrinsic control mechanisms and different sensitivities to environmental factors.

Large-Scale Proteomics of the Cassava Storage Root and Identification of a Target Gene to Reduce Postharvest Deterioration.

Cassava (Manihot esculenta) is the most important root crop in the tropics, but rapid postharvest physiological deterioration (PPD) of the root is a major constraint to commercial cassava production. We established a reliable method for image-based PPD symptom quantification and used label-free quantitative proteomics to generate an extensive cassava root and PPD proteome. Over 2600 unique proteins were identified in the cassava root, and nearly 300 proteins showed significant abundance regulation during PPD. We identified protein abundance modulation in pathways associated with oxidative stress, phenylpropanoid biosynthesis (including scopoletin), the glutathione cycle, fatty acid α-oxidation, folate transformation, and the sulfate reduction II pathway. Increasing protein abundances and enzymatic activities of glutathione-associated enzymes, including glutathione reductases, glutaredoxins, and glutathione S-transferases, indicated a key role for ascorbate/glutathione cycles. Based on combined proteomics data, enzymatic activities, and lipid peroxidation assays, we identified glutathione peroxidase as a candidate for reducing PPD. Transgenic cassava overexpressing a cytosolic glutathione peroxidase in storage roots showed delayed PPD and reduced lipid peroxidation as well as decreased H2O2 accumulation. Quantitative proteomics data from ethene and phenylpropanoid pathways indicate additional gene candidates to further delay PPD. Cassava root proteomics data are available at www.pep2pro.ethz.ch for easy access and comparison with other proteomics data.

Leaf Length Tracker: a novel approach to analyse leaf elongation close to the thermal limit of growth in the field.

Leaf growth in monocot crops such as wheat and barley largely follows the daily temperature course, particularly under cold but humid springtime field conditions. Knowledge of the temperature response of leaf extension, particularly variations close to the thermal limit of growth, helps define physiological growth constraints and breeding-related genotypic differences among cultivars. Here, we present a novel method, called 'Leaf Length Tracker' (LLT), suitable for measuring leaf elongation rates (LERs) of cereals and other grasses with high precision and high temporal resolution under field conditions. The method is based on image sequence analysis, using a marker tracking approach to calculate LERs. We applied the LLT to several varieties of winter wheat (Triticum aestivum), summer barley (Hordeum vulgare), and ryegrass (Lolium perenne), grown in the field and in growth cabinets under controlled conditions. LLT is easy to use and we demonstrate its reliability and precision under changing weather conditions that include temperature, wind, and rain. We found that leaf growth stopped at a base temperature of 0°C for all studied species and we detected significant genotype-specific differences in LER with rising temperature. The data obtained were statistically robust and were reproducible in the tested environments. Using LLT, we were able to detect subtle differences (sub-millimeter) in leaf growth patterns. This method will allow the collection of leaf growth data in a wide range of future field experiments on different graminoid species or varieties under varying environmental or treatment conditions.

Assessing potato tuber diel growth by means of X-ray computed tomography.

The formation and development of belowground organs is difficult to study. X-ray computed tomography (CT) provides the possibility to analyse and interpret subtle volumetric changes of belowground organs such as tubers, storage roots and nodules. Here, we report on the establishment of a method based on a voxel dimension of 240 µm and precision (standard deviation) of 30 µL that allows interpreting growth differences among potato tubers happening within 3 h. Plants were not stressed by the application of X-ray radiation, which was shown both by morphological comparison with control plants and by analysis of lipid peroxidation as a measure of oxidative stress. Diel (24 h) tuber growth fluctuations of three potato genotypes were monitored in soil-filled pots of 10 L. In contrast to the results from previous reports, most tubers grew at similar rates during day and night. Tuber growth was not related to the developmental stage of plants and tubers. Pronounced differences were observed between average growth rates in different tubers within a plant. These results are discussed in the context of restrictions of past methods to study tuber growth and in the context of their potential for the characterization of the formation and development of other belowground plant organs.

High-resolution quantification of root dynamics in split-nutrient rhizoslides reveals rapid and strong proliferation of maize roots in response to local high nitrogen.

The plant's root system is highly plastic, and can respond to environmental stimuli such as high nitrogen (N) in patches. A root may respond to an N patch by selective placement of new lateral roots, and therewith increases root N uptake. This may be a desirable trait in breeding programmes, since it decreases NO3(-) leaching and N2O emission. Roots of maize (Zea mays L.) were grown without N in split-nutrient rhizoslides. One side of the slides was exposed to high N after 15 d of root development, and root elongation was measured for another 15 d, described in a time course model and parameterized. The elongation rates of crown axile roots on the N-treated side of the plant followed a logistic increase to a maximum of 5.3cm d(-1); 95% of the maximum were reached within 4 d. At the same time, on the untreated side, axile root elongation dropped linearly to 1.2cm d(-1) within 6.4 d and stayed constant thereafter. Twice as many lateral roots were formed on the crown axis on the N side compared to the untreated side. Most strikingly, the elongation rates of laterals of the N side increased linearly with most of the roots reaching an asymptote ~8 d after start of the N treatment. By contrast, laterals on the side without N did not show any detectable elongation beyond the first day after their emergence. We conclude that split-nutrient rhizoslides have great potential to improve our knowledge about nitrogen responsiveness and selection for contrasting genotypes.

Quantitative mapping of averaged focal adhesion dynamics in migrating cells by shape normalization.

The spatially ordered formation and disassembly of focal adhesions is a basic requirement for effective cell locomotion. Because focal adhesions couple the contractile actin-myosin network to the substrate, their distribution determines the pattern of traction forces propelling the cell in a certain direction. In the present study, we quantitatively analyzed the spatial patterning of cell-substrate adhesion in migrating cells by mapping averaged focal adhesion growth dynamics to a standardized cell coordinate system. These maps revealed distinct zones of focal adhesion assembly, disassembly and stability and were strongly interrelated with corresponding actin flow and traction force patterns. Moreover, the mapping technique enables precise detection of even minute responses of adhesion dynamics upon targeted signaling perturbations. For example, the partial inhibition of vinculin phosphorylation was followed by the reduced number of newly formed adhesions, whereas growth dynamics of existing adhesions remained unaffected.

FruitPhenoBox - a device for rapid and automated fruit phenotyping of small sample sizes.

BACKGROUND: Fruit appearance of apple (Malus domestica Borkh.) is accession-specific and one of the main criteria for consumer choice. Consequently, fruit appearance is an important selection criterion in the breeding of new cultivars. It is also used for the description of older varieties or landraces. In commercial apple production, sorting devices are used to classify large numbers of fruit from a few cultivars. In contrast, the description of fruit from germplasm collections or breeding programs is based on only a few fruit from many accessions and is mostly performed visually by pomology experts. Such visual ratings are laborious, often difficult to compare and remain subjective. RESULTS: Here we report on a morphometric device, the FruitPhenoBox, for automated fruit weighing and appearance description using computer-based analysis of five images per fruit. Recording of approximately 100 fruit from each of 15 apple cultivars using the FruitPhenoBox was rapid, with an average handling and recording time of less than eleven seconds per fruit. Comparison of fruit images from the 15 apple cultivars identified significant differences in shape index, fruit width, height and weight. Fruit shape was characteristic for each cultivar, while fruit color showed larger variation within sample sets. Assessing a subset of 20 randomly selected fruit per cultivar, fruit height, width and weight were described with a relative margin of error of 2.6%, 2.2%, and 6.2%, respectively, calculated from the mean value of all available fruit. CONCLUSIONS: The FruitPhenoBox allows for the rapid and consistent description of fruit appearance from individual apple accessions. By relating the relative margin of error for fruit width, height and weight description with different sample sizes, it was possible to determine an appropriate fruit sample size to efficiently and accurately describe the recorded traits. Therefore, the FruitPhenoBox is a useful tool for breeding and the description of apple germplasm collections.

Thermal imaging can reveal variation in stay-green functionality of wheat canopies under temperate conditions.

Canopy temperature (CT) is often interpreted as representing leaf activity traits such as photosynthetic rates, gas exchange rates, or stomatal conductance. This interpretation is based on the observation that leaf activity traits correlate with transpiration which affects leaf temperature. Accordingly, CT measurements may provide a basis for high throughput assessments of the productivity of wheat canopies during early grain filling, which would allow distinguishing functional from dysfunctional stay-green. However, whereas the usefulness of CT as a fast surrogate measure of sustained vigor under soil drying is well established, its potential to quantify leaf activity traits under high-yielding conditions is less clear. To better understand sensitivity limits of CT measurements under high yielding conditions, we generated within-genotype variability in stay-green functionality by means of differential short-term pre-anthesis canopy shading that modified the sink:source balance. We quantified the effects of these modifications on stay-green properties through a combination of gold standard physiological measurements of leaf activity and newly developed methods for organ-level senescence monitoring based on timeseries of high-resolution imagery and deep-learning-based semantic image segmentation. In parallel, we monitored CT by means of a pole-mounted thermal camera that delivered continuous, ultra-high temporal resolution CT data. Our results show that differences in stay-green functionality translate into measurable differences in CT in the absence of major confounding factors. Differences amounted to approximately 0.8°C and 1.5°C for a very high-yielding source-limited genotype, and a medium-yielding sink-limited genotype, respectively. The gradual nature of the effects of shading on CT during the stay-green phase underscore the importance of a high measurement frequency and a time-integrated analysis of CT, whilst modest effect sizes confirm the importance of restricting screenings to a limited range of morphological and phenological diversity.

From Neglecting to Including Cultivar-Specific Per Se Temperature Responses: Extending the Concept of Thermal Time in Field Crops.

Predicting plant development, a longstanding goal in plant physiology, involves 2 interwoven components: continuous growth and the progression of growth stages (phenology). Current models for winter wheat and soybean assume species-level growth responses to temperature. We challenge this assumption, suggesting that cultivar-specific temperature responses substantially affect phenology. To investigate, we collected field-based growth and phenology data in winter wheat and soybean over multiple years. We used diverse models, from linear to neural networks, to assess growth responses to temperature at various trait and covariate levels. Cultivar-specific nonlinear models best explained phenology-related cultivar-environment interactions. With cultivar-specific models, additional relations to other stressors than temperature were found. The availability of the presented field phenotyping tools allows incorporating cultivar-specific temperature response functions in future plant physiology studies, which will deepen our understanding of key factors that influence plant development. Consequently, this work has implications for crop breeding and cultivation under adverse climatic conditions.

Frost Damage Index: The Antipode of Growing Degree Days.

Abiotic stresses such as heat and frost limit plant growth and productivity. Image-based field phenotyping methods allow quantifying not only plant growth but also plant senescence. Winter crops show senescence caused by cold spells, visible as declines in leaf area. We accurately quantified such declines by monitoring changes in canopy cover based on time-resolved high-resolution imagery in the field. Thirty-six winter wheat genotypes were measured in multiple years. A concept termed "frost damage index" (FDI) was developed that, in analogy to growing degree days, summarizes frost events in a cumulative way. The measured sensitivity of genotypes to the FDI correlated with visual scorings commonly used in breeding to assess winter hardiness. The FDI concept could be adapted to other factors such as drought or heat stress. While commonly not considered in plant growth modeling, integrating such degradation processes may be key to improving the prediction of plant performance for future climate scenarios.

Flower Mapping in Grasslands With Drones and Deep Learning.

Manual assessment of flower abundance of different flowering plant species in grasslands is a time-consuming process. We present an automated approach to determine the flower abundance in grasslands from drone-based aerial images by using deep learning (Faster R-CNN) object detection approach, which was trained and evaluated on data from five flights at two sites. Our deep learning network was able to identify and classify individual flowers. The novel method allowed generating spatially explicit maps of flower abundance that met or exceeded the accuracy of the manual-count-data extrapolation method while being less labor intensive. The results were very good for some types of flowers, with precision and recall being close to or higher than 90%. Other flowers were detected poorly due to reasons such as lack of enough training data, appearance changes due to phenology, or flowers being too small to be reliably distinguishable on the aerial images. The method was able to give precise estimates of the abundance of many flowering plant species. In the future, the collection of more training data will allow better predictions for the flowers that are not well predicted yet. The developed pipeline can be applied to any sort of aerial object detection problem.

Outdoor Plant Segmentation With Deep Learning for High-Throughput Field Phenotyping on a Diverse Wheat Dataset.

Robust and automated segmentation of leaves and other backgrounds is a core prerequisite of most approaches in high-throughput field phenotyping. So far, the possibilities of deep learning approaches for this purpose have not been explored adequately, partly due to a lack of publicly available, appropriate datasets. This study presents a workflow based on DeepLab v3+ and on a diverse annotated dataset of 190 RGB (350 x 350 pixels) images. Images of winter wheat plants of 76 different genotypes and developmental stages have been acquired throughout multiple years at high resolution in outdoor conditions using nadir view, encompassing a wide range of imaging conditions. Inconsistencies of human annotators in complex images have been quantified, and metadata information of camera settings has been included. The proposed approach achieves an intersection over union (IoU) of 0.77 and 0.90 for plants and soil, respectively. This outperforms the benchmarked machine learning methods which use Support Vector Classifier and/or Random Forrest. The results show that a small but carefully chosen and annotated set of images can provide a good basis for a powerful segmentation pipeline. Compared to earlier methods based on machine learning, the proposed method achieves better performance on the selected dataset in spite of using a deep learning approach with limited data. Increasing the amount of publicly available data with high human agreement on annotations and further development of deep neural network architectures will provide high potential for robust field-based plant segmentation in the near future. This, in turn, will be a cornerstone of data-driven improvement in crop breeding and agricultural practices of global benefit.

Global Wheat Head Detection 2021: An Improved Dataset for Benchmarking Wheat Head Detection Methods.

The Global Wheat Head Detection (GWHD) dataset was created in 2020 and has assembled 193,634 labelled wheat heads from 4700 RGB images acquired from various acquisition platforms and 7 countries/institutions. With an associated competition hosted in Kaggle, GWHD_2020 has successfully attracted attention from both the computer vision and agricultural science communities. From this first experience, a few avenues for improvements have been identified regarding data size, head diversity, and label reliability. To address these issues, the 2020 dataset has been reexamined, relabeled, and complemented by adding 1722 images from 5 additional countries, allowing for 81,553 additional wheat heads. We now release in 2021 a new version of the Global Wheat Head Detection dataset, which is bigger, more diverse, and less noisy than the GWHD_2020 version.

Tyrosine phosphorylation of vinculin at position 1065 modifies focal adhesion dynamics and cell tractions.

Focal adhesions (FAs) connect the cellular actin cytoskeleton via integrin with the extracellular matrix. They comprise of many structural and signaling proteins which are highly dynamic, well regulated, and responsible for the sensing of physical properties from the environment. Vinculin is a protein that incorporates all these functions. Here, we investigated the phosphorylation of Y1065 in the activation/regulation of vinculin. We used different vinculin mutants mimicking either a permanently activated or inhibited phosphorylation site at position 1065. Using these mutants, we determined their influence on the exchange dynamics and cell forces using fluorescence recovery after photobleaching and traction microscopy. The results indicate that phosphorylation at Y1065 significantly increases the amount of freely exchanging vinculin within FAs whereas inhibition of this phosphorylation site leads to an uncontrolled exchange of vinculin and reduced adhesive cell forces. In conclusion, we show that phosphorylation on position Y1065 is essential for accurate incorporation of vinculin into FAs and mechanical behavior of cells.

Becoming stable and strong: the interplay between vinculin exchange dynamics and adhesion strength during adhesion site maturation.

The coordinated formation and release of focal adhesions is necessary for cell attachment and migration. According to current models, these processes are caused by temporal variations in protein composition. Protein incorporation into focal adhesions is believed to be controlled by phosphorylation. Here, we tested the exchange dynamics of GFP-vinculin as marker protein of focal adhesions using the method of Fluorescence Recovery After Photobleaching. The relevance of the phosphorylation state of the protein, the age of focal adhesions and the acting force were investigated. For stable focal adhesions of stationary keratinocytes, we determined an exchangeable vinculin fraction of 52% and a recovery halftime of 57 s. Nascent focal adhesions of moving cells contained a fraction of exchanging vinculin of 70% with a recovery halftime of 36 s. Upon maturation, mean saturation values and recovery halftimes decreased to levels of 49% and 42 s, respectively. Additionally, the fraction of stably incorporated vinculin increased with cell forces and decreased with vinculin phosphorylation within these sites. Experiments on a nonphosphorylatable vinculin mutant construct at phosphorylation site tyr1065 confirmed the direct interplay between phosphorylation and exchange dynamics of adhesion proteins during adhesion site maturation.

PhenoCams for Field Phenotyping: Using Very High Temporal Resolution Digital Repeated Photography to Investigate Interactions of Growth, Phenology, and Harvest Traits.

Understanding the interaction of plant growth with environmental conditions is crucial to increase the resilience of current cropping systems to a changing climate. Here, we investigate PhenoCams as a high-throughput approach for field phenotyping experiments to assess growth dynamics of many different genotypes simultaneously in high temporal (daily) resolution. First, we develop a method that extracts a daily phenological signal that is normalized for the different viewing geometries of the pixels within the images. Second, we investigate the extraction of the in season traits of early vigor, leaf area index (LAI), and senescence dynamic from images of a soybean (Glycine max) field phenotyping experiment and show that it is possible to rate early vigor, senescence dynamics, and track the LAI development between LAI 1 and 4.5. Third, we identify the start of green up, green peak, senescence peak, and end of senescence in the phenological signal. Fourth, we extract the timing of these points and show how this information can be used to assess the impact of phenology on harvest traits (yield, thousand kernel weight, and oil content). The results demonstrate that PhenoCams can track growth dynamics and fill the gap of high temporal monitoring in field phenotyping experiments.

Repeated Multiview Imaging for Estimating Seedling Tiller Counts of Wheat Genotypes Using Drones.

Early generation breeding nurseries with thousands of genotypes in single-row plots are well suited to capitalize on high throughput phenotyping. Nevertheless, methods to monitor the intrinsically hard-to-phenotype early development of wheat are yet rare. We aimed to develop proxy measures for the rate of plant emergence, the number of tillers, and the beginning of stem elongation using drone-based imagery. We used RGB images (ground sampling distance of 3 mm pixel-1) acquired by repeated flights (≥ 2 flights per week) to quantify temporal changes of visible leaf area. To exploit the information contained in the multitude of viewing angles within the RGB images, we processed them to multiview ground cover images showing plant pixel fractions. Based on these images, we trained a support vector machine for the beginning of stem elongation (GS30). Using the GS30 as key point, we subsequently extracted plant and tiller counts using a watershed algorithm and growth modeling, respectively. Our results show that determination coefficients of predictions are moderate for plant count (R 2 = 0.52), but strong for tiller count (R 2 = 0.86) and GS30 (R 2 = 0.77). Heritabilities are superior to manual measurements for plant count and tiller count, but inferior for GS30 measurements. Increasing the selection intensity due to throughput may overcome this limitation. Multiview image traits can replace hand measurements with high efficiency (85-223%). We therefore conclude that multiview images have a high potential to become a standard tool in plant phenomics.