High-resolution time-resolved imaging of in vitro Arabidopsis rosette growth

Development of a mobile, high-throughput, and low-cost image-based plant growth phenotyping system

Nondestructive plant phenotyping forms a key technique for unraveling molecular processes underlying plant development and response to the environment. While the emergence of high-throughput phenotyping facilities can further our understanding of plant development and stress responses, their high costs greatly hinder scientific progress. To democratize high-throughput plant phenotyping, we developed sets of low-cost image- and weight-based devices to monitor plant shoot growth and evapotranspiration. We paired these devices to a suite of computational pipelines for integrated and straightforward data analysis. The developed tools were validated for their suitability for large genetic screens by evaluating a cowpea (Vigna unguiculata) diversity panel for responses to drought stress. The observed natural variation was used as an input for a genome-wide association study, from which we identified nine genetic loci that might contribute to cowpea drought resilience during early vegetative development. The homologs of the candidate genes were identified in Arabidopsis (Arabidopsis thaliana) and subsequently evaluated for their involvement in drought stress by using available T-DNA insertion mutant lines. These results demonstrate the varied applicability of this low-cost phenotyping system. In the future, we foresee these setups facilitating the identification of genetic components of growth, plant architecture, and stress tolerance across a wide variety of plant species.

Ectopic Expression of Distinct PLC Genes Identifies 'Compactness' as a Possible Architectural Shoot Strategy to Cope with Drought Stress

Phospholipase C (PLC) has been implicated in several stress responses, including drought. Overexpression (OE) of PLC has been shown to improve drought tolerance in various plant species. Arabidopsis contains nine PLC genes, which are subdivided into four clades. Earlier, OE of PLC3, PLC5 or PLC7 was found to increase Arabidopsis' drought tolerance. Here, we confirm this for three other PLCs: PLC2, the only constitutively expressed AtPLC; PLC4, reported to have reduced salt tolerance and PLC9, of which the encoded enzyme was presumed to be catalytically inactive. To compare each PLC and to discover any other potential phenotype, two independent OE lines of six AtPLC genes, representing all four clades, were simultaneously monitored with the GROWSCREEN-FLUORO phenotyping platform, under both control- and mild-drought conditions. To investigate which tissues were most relevant to achieving drought survival, we additionally expressed AtPLC5 using 13 different cell- or tissue-specific promoters. While no significant differences in plant size, biomass or photosynthesis were found between PLC lines and wild-type (WT) plants, all PLC-OE lines, as well as those tissue-specific lines that promoted drought survival, exhibited a stronger decrease in 'convex hull perimeter' (= increase in 'compactness') under water deprivation compared to WT. Increased compactness has not been associated with drought or decreased water loss before although a hyponastic decrease in compactness in response to increased temperatures has been associated with water loss. We propose that the increased compactness could lead to decreased water loss and potentially provide a new breeding trait to select for drought tolerance.

From Organelle Morphology to Whole-Plant Phenotyping: A Phenotypic Detection Method Based on Deep Learning

The analysis of plant phenotype parameters is closely related to breeding, so plant phenotype research has strong practical significance. This paper used deep learning to classify Arabidopsis thaliana from the macro (plant) to the micro level (organelle). First, the multi-output model identifies Arabidopsis accession lines and regression to predict Arabidopsis's 22-day growth status. The experimental results showed that the model had excellent performance in identifying Arabidopsis lines, and the model's classification accuracy was 99.92%. The model also had good performance in predicting plant growth status, and the regression prediction of the model root mean square error (RMSE) was 1.536. Next, a new dataset was obtained by increasing the time interval of Arabidopsis images, and the model's performance was verified at different time intervals. Finally, the model was applied to classify Arabidopsis organelles to verify the model's generalizability. Research suggested that deep learning will broaden plant phenotype detection methods. Furthermore, this method will facilitate the design and development of a high-throughput information collection platform for plant phenotypes.

AraDQ: an automated digital phenotyping software for quantifying disease symptoms of flood-inoculated Arabidopsis seedlings

BACKGROUND

Plant scientists have largely relied on pathogen growth assays and/or transcript analysis of stress-responsive genes for quantification of disease severity and susceptibility. These methods are destructive to plants, labor-intensive, and time-consuming, thereby limiting their application in real-time, large-scale studies. Image-based plant phenotyping is an alternative approach that enables automated measurement of various symptoms. However, most of the currently available plant image analysis tools require specific hardware platform and vendor specific software packages, and thus, are not suited for researchers who are not primarily focused on plant phenotyping. In this study, we aimed to develop a digital phenotyping tool to enhance the speed, accuracy, and reliability of disease quantification in Arabidopsis.

RESULTS

Here, we present the Arabidopsis Disease Quantification (AraDQ) image analysis tool for examination of flood-inoculated Arabidopsis seedlings grown on plates containing plant growth media. It is a cross-platform application program with a user-friendly graphical interface that contains highly accurate deep neural networks for object detection and segmentation. The only prerequisite is that the input image should contain a fixed-sized 24-color balance card placed next to the objects of interest on a white background to ensure reliable and reproducible results, regardless of the image acquisition method. The image processing pipeline automatically calculates 10 different colors and morphological parameters for individual seedlings in the given image, and disease-associated phenotypic changes can be easily assessed by comparing plant images captured before and after infection. We conducted two case studies involving bacterial and plant mutants with reduced virulence and disease resistance capabilities, respectively, and thereby demonstrated that AraDQ can capture subtle changes in plant color and morphology with a high level of sensitivity.

CONCLUSIONS

AraDQ offers a simple, fast, and accurate approach for image-based quantification of plant disease symptoms using various parameters. Its fully automated pipeline neither requires prior image processing nor costly hardware setups, allowing easy implementation of the software by researchers interested in digital phenotyping of diseased plants.

Low-cost and automated phenotyping system "Phenomenon" for multi-sensor in situ monitoring in plant in vitro culture

BACKGROUND

The current development of sensor technologies towards ever more cost-effective and powerful systems is steadily increasing the application of low-cost sensors in different horticultural sectors. In plant in vitro culture, as a fundamental technique for plant breeding and plant propagation, the majority of evaluation methods to describe the performance of these cultures are based on destructive approaches, limiting data to unique endpoint measurements. Therefore, a non-destructive phenotyping system capable of automated, continuous and objective quantification of in vitro plant traits is desirable.

RESULTS

An automated low-cost multi-sensor system acquiring phenotypic data of plant in vitro cultures was developed and evaluated. Unique hardware and software components were selected to construct a xyz-scanning system with an adequate accuracy for consistent data acquisition. Relevant plant growth predictors, such as projected area of explants and average canopy height were determined employing multi-sensory imaging and various developmental processes could be monitored and documented. The validation of the RGB image segmentation pipeline using a random forest classifier revealed very strong correlation with manual pixel annotation. Depth imaging by a laser distance sensor of plant in vitro cultures enabled the description of the dynamic behavior of the average canopy height, the maximum plant height, but also the culture media height and volume. Projected plant area in depth data by RANSAC (random sample consensus) segmentation approach well matched the projected plant area by RGB image processing pipeline. In addition, a successful proof of concept for in situ spectral fluorescence monitoring was achieved and challenges of thermal imaging were documented. Potential use cases for the digital quantification of key performance parameters in research and commercial application are discussed.

CONCLUSION

The technical realization of "Phenomenon" allows phenotyping of plant in vitro cultures under highly challenging conditions and enables multi-sensory monitoring through closed vessels, ensuring the aseptic status of the cultures. Automated sensor application in plant tissue culture promises great potential for a non-destructive growth analysis enhancing commercial propagation as well as enabling research with novel digital parameters recorded over time.

PhenoWell®-A novel screening system for soil-grown plants

As agricultural production is reaching its limits regarding outputs and land use, the need to further improve crop yield is greater than ever. The limited translatability from in vitro lab results into more natural growth conditions in soil remains problematic. Although considerable progress has been made in developing soil-growth assays to tackle this bottleneck, the majority of these assays use pots or whole trays, making them not only space- and resource-intensive, but also hampering the individual treatment of plants. Therefore, we developed a flexible and compact screening system named PhenoWell® in which individual seedlings are grown in wells filled with soil allowing single-plant treatments. The system makes use of an automated image-analysis pipeline that extracts multiple growth parameters from individual seedlings over time, including projected rosette area, relative growth rate, compactness, and stockiness. Macronutrient, hormone, salt, osmotic, and drought stress treatments were tested in the PhenoWell® system. The system is also optimized for maize with results that are consistent with Arabidopsis while different in amplitude. We conclude that the PhenoWell® system enables a high-throughput, precise, and uniform application of a small amount of solution to individually soil-grown plants, which increases the replicability and reduces variability and compound usage.

Time-Series Growth Prediction Model Based on U-Net and Machine Learning in Arabidopsis

Yield prediction for crops is essential information for food security. A high-throughput phenotyping platform (HTPP) generates the data of the complete life cycle of a plant. However, the data are rarely used for yield prediction because of the lack of quality image analysis methods, yield data associated with HTPP, and the time-series analysis method for yield prediction. To overcome limitations, this study employed multiple deep learning (DL) networks to extract high-quality HTTP data, establish an association between HTTP data and the yield performance of crops, and select essential time intervals using machine learning (ML). The images of Arabidopsis were taken 12 times under environmentally controlled HTPP over 23 days after sowing (DAS). First, the features from images were extracted using DL network U-Net with SE-ResXt101 encoder and divided into early (15-21 DAS) and late (∼21-23 DAS) pre-flowering developmental stages using the physiological characteristics of the Arabidopsis plant. Second, the late pre-flowering stage at 23 DAS can be predicted using the ML algorithm XGBoost, based only on a portion of the early pre-flowering stage (17-21 DAS). This was confirmed using an additional biological experiment (P < 0.01). Finally, the projected area (PA) was estimated into fresh weight (FW), and the correlation coefficient between FW and predicted FW was calculated as 0.85. This was the first study that analyzed time-series data to predict the FW of related but different developmental stages and predict the PA. The results of this study were informative and enabled the understanding of the FW of Arabidopsis or yield of leafy plants and total biomass consumed in vertical farming. Moreover, this study highlighted the reduction of time-series data for examining interesting traits and future application of time-series analysis in various HTPPs.

Decoding Plant-Environment Interactions That Influence Crop Agronomic Traits

To ensure food security in the face of increasing global demand due to population growth and progressive urbanization, it will be crucial to integrate emerging technologies in multiple disciplines to accelerate overall throughput of gene discovery and crop breeding. Plant agronomic traits often appear during the plants' later growth stages due to the cumulative effects of their lifetime interactions with the environment. Therefore, decoding plant-environment interactions by elucidating plants' temporal physiological responses to environmental changes throughout their lifespans will facilitate the identification of genetic and environmental factors, timing and pathways that influence complex end-point agronomic traits, such as yield. Here, we discuss the expected role of the life-course approach to monitoring plant and crop health status in improving crop productivity by enhancing the understanding of plant-environment interactions. We review recent advances in analytical technologies for monitoring health status in plants based on multi-omics analyses and strategies for integrating heterogeneous datasets from multiple omics areas to identify informative factors associated with traits of interest. In addition, we showcase emerging phenomics techniques that enable the noninvasive and continuous monitoring of plant growth by various means, including three-dimensional phenotyping, plant root phenotyping, implantable/injectable sensors and affordable phenotyping devices. Finally, we present an integrated review of analytical technologies and applications for monitoring plant growth, developed across disciplines, such as plant science, data science and sensors and Internet-of-things technologies, to improve plant productivity.

High-throughput phenotyping of brachiaria grass seeds using free access tool for analyzing X-ray images

New approaches based on image analysis can assist in phenotyping of biological characteristics, serving as support for decision-making in modern agribusiness. The aim of this study was to propose a method of high-throughput phenotyping of free access for processing of 2D X-ray images of brachiaria grass (Brachiaria ruziziensis cv. Ruziziensis) seeds, as well as correlate the parameters linked to the physiological potential of the seeds. The study was carried out by means of automated analysis of X-ray images of seeds in which a macro, called PhenoXray, was developed, responsible for digital image processing, for which a series of descriptors were obtained. After the X-ray analysis, a germination test was performed on the seeds and, from this, variables related to the physiological quality of the seeds were obtained. The use of the macro PhenoXray allowed large-scale phenotyping of seed X-rays in a simple, rapid, robust, and totally free manner. This study confirmed that the methodology is efficient for obtaining morphometric data and tissue integrity data in Brachiaria ruziziensis seeds and that parameters such as relative density, integrated density, and seed filling are closely related to the physiological attributes of seed quality.

High-Throughput Phenotyping (HTP) Data Reveal Dosage Effect at Growth Stages in Arabidopsis thaliana Irradiated by Gamma Rays

The effects of radiation dosages on plant species are quantitatively presented as the lethal dose or the dose required for growth reduction in mutation breeding. However, lethal dose and growth reduction fail to provide dynamic growth behavior information such as growth rate after irradiation. Irradiated seeds of Arabidopsis were grown in an environmentally controlled high-throughput phenotyping (HTP) platform to capture growth images that were analyzed with machine learning algorithms. Analysis of digital phenotyping data revealed unique growth patterns following treatments below LD50 value at 641 Gy. Plants treated with 100-Gy gamma irradiation showed almost identical growth pattern compared with wild type; the hormesis effect was observed >21 days after sowing. In 200 Gy-treated plants, a uniform growth pattern but smaller rosette areas than the wild type were seen (p < 0.05). The shift between vegetative and reproductive stages was not retarded by irradiation at 200 and 300 Gy although growth inhibition was detected under the same irradiation dose. Results were validated using 200 and 300 Gy doses with HTP in a separate study. To our knowledge, this is the first study to apply a HTP platform to measure and analyze the dosage effect of radiation in plants. The method enabled an in-depth analysis of growth patterns, which could not be detected previously due to a lack of time-series data. This information will improve our knowledge about the effects of radiation in model plant species and crops.

All roads lead to growth: imaging-based and biochemical methods to measure plant growth

Plant growth is a highly complex biological process that involves innumerable interconnected biochemical and signalling pathways. Many different techniques have been developed to measure growth, unravel the various processes that contribute to plant growth, and understand how a complex interaction between genotype and environment determines the growth phenotype. Despite this complexity, the term 'growth' is often simplified by researchers; depending on the method used for quantification, growth is viewed as an increase in plant or organ size, a change in cell architecture, or an increase in structural biomass. In this review, we summarise the cellular and molecular mechanisms underlying plant growth, highlight state-of-the-art imaging and non-imaging-based techniques to quantitatively measure growth, including a discussion of their advantages and drawbacks, and suggest a terminology for growth rates depending on the type of technique used.

cis-Cinnamic acid is a natural plant growth-promoting compound

Agrochemicals provide vast potential to improve plant productivity, because they are easy to implement at low cost while not being restricted by species barriers as compared with breeding strategies. Despite the general interest, only a few compounds with growth-promoting activity have been described so far. Here, we add cis-cinnamic acid (c-CA) to the small portfolio of existing plant growth stimulators. When applied at low micromolar concentrations to Arabidopsis roots, c-CA stimulates both cell division and cell expansion in leaves. Our data support a model explaining the increase in shoot biomass as the consequence of a larger root system, which allows the plant to explore larger areas for resources. The requirement of the cis-configuration for the growth-promoting activity of CA was validated by implementing stable structural analogs of both cis- and trans-CA in this study. In a complementary approach, we used specific light conditions to prevent cis/trans-isomerization of CA during the experiment. In both cases, the cis-form stimulated plant growth, whereas the trans-form was inactive. Based on these data, we conclude that c-CA is an appealing lead compound representing a novel class of growth-promoting agrochemicals. Unraveling the underlying molecular mechanism could lead to the development of innovative strategies for boosting plant biomass.

Leaf economics and slow-fast adaptation across the geographic range of Arabidopsis thaliana

Life history strategies of most organisms are constrained by resource allocation patterns that follow a 'slow-fast continuum'. It opposes slow growing and long-lived organisms with late investment in reproduction to those that grow faster, have earlier and larger reproductive effort and a short longevity. In plants, the Leaf Economics Spectrum (LES) depicts a leaf-level trade-off between the rate of carbon assimilation and leaf lifespan, as stressed in functional ecology from interspecific comparative studies. However, it is still unclear how the LES is connected to the slow-fast syndrome. Interspecific comparisons also impede a deep exploration of the linkage between LES variation and adaptation to climate. Here, we measured growth, morpho-physiological and life-history traits, at both the leaf and whole-plant levels, in 378 natural accessions of Arabidopsis thaliana. We found that the LES is tightly linked to variation in whole-plant functioning, and aligns with the slow-fast continuum. A genetic analysis further suggested that phenotypic differentiation results from the selection of different slow-fast strategies in contrasted climates. Slow growing and long-lived plants were preferentially found in cold and arid habitats while fast growing and short-lived ones in more favorable habitats. Our findings shed light on the role of the slow-fast continuum for plant adaptation to climate. More broadly, they encourage future studies to bridge functional ecology, genetics and evolutionary biology to improve our understanding of plant adaptation to environmental changes.

Drought resistance is mediated by divergent strategies in closely related Brassicaceae

Droughts cause severe crop losses worldwide and climate change is projected to increase their prevalence in the future. Similar to the situation for many crops, the reference plant Arabidopsis thaliana (Ath) is considered drought-sensitive, whereas, as we demonstrate, its close relatives Arabidopsis lyrata (Aly) and Eutrema salsugineum (Esa) are drought-resistant. To understand the molecular basis for this plasticity we conducted a deep phenotypic, biochemical and transcriptomic comparison using developmentally matched plants. We demonstrate that Aly responds most sensitively to decreasing water availability with early growth reduction, metabolic adaptations and signaling network rewiring. By contrast, Esa is in a constantly prepared mode as evidenced by high basal proline levels, ABA signaling transcripts and late growth responses. The stress-sensitive Ath responds later than Aly and earlier than Esa, although its responses tend to be more extreme. All species detect water scarcity with similar sensitivity; response differences are encoded in downstream signaling and response networks. Moreover, several signaling genes expressed at higher basal levels in both Aly and Esa have been shown to increase water-use efficiency and drought resistance when overexpressed in Ath. Our data demonstrate contrasting strategies of closely related Brassicaceae to achieve drought resistance.

A photometric stereo-based 3D imaging system using computer vision and deep learning for tracking plant growth

BACKGROUND

Tracking and predicting the growth performance of plants in different environments is critical for predicting the impact of global climate change. Automated approaches for image capture and analysis have allowed for substantial increases in the throughput of quantitative growth trait measurements compared with manual assessments. Recent work has focused on adopting computer vision and machine learning approaches to improve the accuracy of automated plant phenotyping. Here we present PS-Plant, a low-cost and portable 3D plant phenotyping platform based on an imaging technique novel to plant phenotyping called photometric stereo (PS).

RESULTS

We calibrated PS-Plant to track the model plant Arabidopsis thaliana throughout the day-night (diel) cycle and investigated growth architecture under a variety of conditions to illustrate the dramatic effect of the environment on plant phenotype. We developed bespoke computer vision algorithms and assessed available deep neural network architectures to automate the segmentation of rosettes and individual leaves, and extract basic and more advanced traits from PS-derived data, including the tracking of 3D plant growth and diel leaf hyponastic movement. Furthermore, we have produced the first PS training data set, which includes 221 manually annotated Arabidopsis rosettes that were used for training and data analysis (1,768 images in total). A full protocol is provided, including all software components and an additional test data set.

CONCLUSIONS

PS-Plant is a powerful new phenotyping tool for plant research that provides robust data at high temporal and spatial resolutions. The system is well-suited for small- and large-scale research and will help to accelerate bridging of the phenotype-to-genotype gap.

Image-based methods for phenotyping growth dynamics and fitness components in Arabidopsis thaliana

BACKGROUND

The model species Arabidopsis thaliana has extensive resources to investigate intraspecific trait variability and the genetic bases of ecologically relevant traits. However, the cost of equipment and software required for high-throughput phenotyping is often a bottleneck for large-scale studies, such as mutant screening or quantitative genetics analyses. Simple tools are needed for the measurement of fitness-related traits, like relative growth rate and fruit production, without investment in expensive infrastructures. Here, we describe methods that enable the estimation of biomass accumulation and fruit number from the analysis of rosette and inflorescence images taken with a regular camera.

RESULTS

We developed two models to predict plant dry mass and fruit number from the parameters extracted with the analysis of rosette and inflorescence images. Predictive models were trained by sacrificing growing individuals for dry mass estimation, and manually measuring a fraction of individuals for fruit number at maturity. Using a cross-validation approach, we showed that quantitative parameters extracted from image analysis predicts more 90% of both plant dry mass and fruit number. When used on 451 natural accessions, the method allowed modeling growth dynamics, including relative growth rate, throughout the life cycle of various ecotypes. Estimated growth-related traits had high heritability (0.65 < H2 < 0.93), as well as estimated fruit number (H2 = 0.68). In addition, we validated the method for estimating fruit number with rev5, a mutant with increased flower abortion.

CONCLUSIONS

The method we propose here is an application of automated computerization of plant images with ImageJ, and subsequent statistical modeling in R. It allows plant biologists to measure growth dynamics and fruit number in hundreds of individuals with simple computing steps that can be repeated and adjusted to a wide range of laboratory conditions. It is thus a flexible toolkit for the measurement of fitness-related traits in large populations of a model species.

PlantSize Offers an Affordable, Non-destructive Method to Measure Plant Size and Color in Vitro

Plant size, shape and color are important parameters of plants, which have traditionally been measured by destructive and time-consuming methods. Non-destructive image analysis is an increasingly popular technology to characterize plant development in time. High throughput automatic phenotyping platforms can simultaneously analyze multiple morphological and physiological parameters of hundreds or thousands of plants. Such platforms are, however, expensive and are not affordable for many laboratories. Moreover, determination of basic parameters is sufficient for most studies. Here we describe a non-invasive method, which simultaneously measures basic morphological and physiological parameters of in vitro cultured plants. Changes of plant size, shape and color is monitored by repeated photography with a commercial digital camera using neutral white background. Images are analyzed with the MatLab-based computer application PlantSize, which simultaneously calculates several parameters including rosette size, convex area, convex ratio, chlorophyll and anthocyanin contents of all plants identified on the image. Numerical data are exported in MS Excel-compatible format. Subsequent data processing provides information on growth rates, chlorophyll and anthocyanin contents. Proof-of-concept validation of the imaging technology was demonstrated by revealing small but significant differences between wild type and transgenic Arabidopsis plants overexpressing the HSFA4A transcription factor or the hsfa4a knockout mutant, subjected to different stress conditions. While HSFA4A overexpression was associated with better growth, higher chlorophyll and lower anthocyanin content in saline conditions, the knockout hsfa4a mutant showed hypersensitivity to various stresses. Morphological differences were revealed by comparing rosette size, shape and color of wild type plants with phytochrome B (phyB-9) mutant. While the technology was developed with Arabidopsis plants, it is suitable to characterize plants of other species including crops, in a simple, affordable and fast way. PlantSize is publicly available (http://www.brc.hu/pub/psize/index.html).

Image-based methods for phenotyping growth dynamics and fitness components in Arabidopsis thaliana

BACKGROUND

The model species Arabidopsis thaliana has extensive resources to investigate intraspecific trait variability and the genetic bases of ecologically relevant traits. However, the cost of equipment and software required for high-throughput phenotyping is often a bottleneck for large-scale studies, such as mutant screening or quantitative genetics analyses. Simple tools are needed for the measurement of fitness-related traits, like relative growth rate and fruit production, without investment in expensive infrastructures. Here, we describe methods that enable the estimation of biomass accumulation and fruit number from the analysis of rosette and inflorescence images taken with a regular camera.

RESULTS

We developed two models to predict plant dry mass and fruit number from the parameters extracted with the analysis of rosette and inflorescence images. Predictive models were trained by sacrificing growing individuals for dry mass estimation, and manually measuring a fraction of individuals for fruit number at maturity. Using a cross-validation approach, we showed that quantitative parameters extracted from image analysis predicts more 90% of both plant dry mass and fruit number. When used on 451 natural accessions, the method allowed modeling growth dynamics, including relative growth rate, throughout the life cycle of various ecotypes. Estimated growth-related traits had high heritability (0.65 < H² < 0.93), as well as estimated fruit number (H² = 0.68). In addition, we validated the method for estimating fruit number with rev5, a mutant with increased flower abortion.

CONCLUSIONS

The method we propose here is an application of automated computerization of plant images with ImageJ, and subsequent statistical modeling in R. It allows plant biologists to measure growth dynamics and fruit number in hundreds of individuals with simple computing steps that can be repeated and adjusted to a wide range of laboratory conditions. It is thus a flexible toolkit for the measurement of fitness-related traits in large populations of a model species.

A "Do-It-Yourself" phenotyping system: measuring growth and morphology throughout the diel cycle in rosette shaped plants

BACKGROUND

Improvements in high-throughput phenotyping technologies are rapidly expanding the scope and capacity of plant biology studies to measure growth traits. Nevertheless, the costs of commercial phenotyping equipment and infrastructure remain prohibitively expensive for wide-scale uptake, while academic solutions can require significant local expertise. Here we present a low-cost methodology for plant biologists to build their own phenotyping system for quantifying growth rates and phenotypic characteristics of Arabidopsis thaliana rosettes throughout the diel cycle.

RESULTS

We constructed an image capture system consisting of a near infra-red (NIR, 940 nm) LED panel with a mounted Raspberry Pi NoIR camera and developed a MatLab-based software module (iDIEL Plant) to characterise rosette expansion. Our software was able to accurately segment and characterise multiple rosettes within an image, regardless of plant arrangement or genotype, and batch process image sets. To further validate our system, wild-type Arabidopsis plants (Col-0) and two mutant lines with reduced Rubisco contents, pale leaves and slow growth phenotypes (1a3b and 1a2b) were grown on a single plant tray. Plants were imaged from 9 to 24 days after germination every 20 min throughout the 24 h light-dark growth cycle (i.e. the diel cycle). The resulting dataset provided a dynamic and uninterrupted characterisation of differences in rosette growth and expansion rates over time for the three lines tested.

CONCLUSION

Our methodology offers a straightforward solution for setting up automated, scalable and low-cost phenotyping facilities in a wide range of lab environments that could greatly increase the processing power and scalability of Arabidopsis soil growth experiments.

Unlocking the potential of plant phenotyping data through integration and data-driven approaches

Plant phenotyping has emerged as a comprehensive field of research as the result of significant advancements in the application of imaging sensors for high-throughput data collection. The flip side is the risk of drowning in the massive amounts of data generated by automated phenotyping systems. Currently, the major challenge lies in data management, on the level of data annotation and proper metadata collection, and in progressing towards synergism across data collection and analyses. Progress in data analyses includes efforts towards the integration of phenotypic and -omics data resources for bridging the phenotype-genotype gap and obtaining in-depth insights into fundamental plant processes.

The transcriptional repressor complex FRS7-FRS12 regulates flowering time and growth in Arabidopsis

Most living organisms developed systems to efficiently time environmental changes. The plant-clock acts in coordination with external signals to generate output responses determining seasonal growth and flowering time. Here, we show that two Arabidopsis thaliana transcription factors, FAR1 RELATED SEQUENCE 7 (FRS7) and FRS12, act as negative regulators of these processes. These proteins accumulate particularly in short-day conditions and interact to form a complex. Loss-of-function of FRS7 and FRS12 results in early flowering plants with overly elongated hypocotyls mainly in short days. We demonstrate by molecular analysis that FRS7 and FRS12 affect these developmental processes in part by binding to the promoters and repressing the expression of GIGANTEA and PHYTOCHROME INTERACTING FACTOR 4 as well as several of their downstream signalling targets. Our data reveal a molecular machinery that controls the photoperiodic regulation of flowering and growth and offer insight into how plants adapt to seasonal changes.

The significance of image compression in plant phenotyping applications

We are currently witnessing an increasingly higher throughput in image-based plant phenotyping experiments. The majority of imaging data are collected using complex automated procedures and are then post-processed to extract phenotyping-related information. In this article, we show that the image compression used in such procedures may compromise phenotyping results and this needs to be taken into account. We use three illuminating proof-of-concept experiments that demonstrate that compression (especially in the most common lossy JPEG form) affects measurements of plant traits and the errors introduced can be high. We also systematically explore how compression affects measurement fidelity, quantified as effects on image quality, as well as errors in extracted plant visual traits. To do so, we evaluate a variety of image-based phenotyping scenarios, including size and colour of shoots, leaf and root growth. To show that even visual impressions can be used to assess compression effects, we use root system images as examples. Overall, we find that compression has a considerable effect on several types of analyses (albeit visual or quantitative) and that proper care is necessary to ensure that this choice does not affect biological findings. In order to avoid or at least minimise introduced measurement errors, for each scenario, we derive recommendations and provide guidelines on how to identify suitable compression options in practice. We also find that certain compression choices can offer beneficial returns in terms of reducing the amount of data storage without compromising phenotyping results. This may enable even higher throughput experiments in the future.

A Journey Through a Leaf: Phenomics Analysis of Leaf Growth in Arabidopsis thaliana

In Arabidopsis, leaves contribute to the largest part of the aboveground biomass. In these organs, light is captured and converted into chemical energy, which plants use to grow and complete their life cycle. Leaves emerge as a small pool of cells at the vegetative shoot apical meristem and develop into planar, complex organs through different interconnected cellular events. Over the last decade, numerous phenotyping techniques have been developed to visualize and quantify leaf size and growth, leading to the identification of numerous genes that contribute to the final size of leaves. In this review, we will start at the Arabidopsis rosette level and gradually zoom in from a macroscopic view on leaf growth to a microscopic and molecular view. Along this journey, we describe different techniques that have been key to identify important events during leaf development and discuss approaches that will further help unraveling the complex cellular and molecular mechanisms that underlie leaf growth.

Measurement of plant growth in view of an integrative analysis of regulatory networks

As the regulatory networks of growth at the cellular level are elucidated at a fast pace, their complexity is not reduced; on the contrary, the tissue, organ and even whole-plant level affect cell proliferation and expansion by means of development-induced and environment-induced signaling events in growth regulatory processes. Measurement of growth across different levels aids in gaining a mechanistic understanding of growth, and in defining the spatial and temporal resolution of sampling strategies for molecular analyses in the model Arabidopsis thaliana and increasingly also in crop species. The latter claim their place at the forefront of plant research, since global issues and future needs drive the translation from laboratory model-acquired knowledge of growth processes to improvements in crop productivity in field conditions.

Phytotyping(4D) : a light-field imaging system for non-invasive and accurate monitoring of spatio-temporal plant growth

Integrative studies of plant growth require spatially and temporally resolved information from high-throughput imaging systems. However, analysis and interpretation of conventional two-dimensional images is complicated by the three-dimensional nature of shoot architecture and by changes in leaf position over time, termed hyponasty. To solve this problem, Phytotyping(4D) uses a light-field camera that simultaneously provides a focus image and a depth image, which contains distance information about the object surface. Our automated pipeline segments the focus images, integrates depth information to reconstruct the three-dimensional architecture, and analyses time series to provide information about the relative expansion rate, the timing of leaf appearance, hyponastic movement, and shape for individual leaves and the whole rosette. Phytotyping(4D) was calibrated and validated using discs of known sizes, and plants tilted at various orientations. Information from this analysis was integrated into the pipeline to allow error assessment during routine operation. To illustrate the utility of Phytotyping(4D) , we compare diurnal changes in Arabidopsis thaliana wild-type Col-0 and the starchless pgm mutant. Compared to Col-0, pgm showed very low relative expansion rate in the second half of the night, a transiently increased relative expansion rate at the onset of light period, and smaller hyponastic movement including delayed movement after dusk, both at the level of the rosette and individual leaves. Our study introduces light-field camera systems as a tool to accurately measure morphological and growth-related features in plants.

Molecular systems governing leaf growth: from genes to networks

Arabidopsis leaf growth consists of a complex sequence of interconnected events involving cell division and cell expansion, and requiring multiple levels of genetic regulation. With classical genetics, numerous leaf growth regulators have been identified, but the picture is far from complete. With the recent advances made in quantitative phenotyping, the study of the quantitative, dynamic, and multifactorial features of leaf growth is now facilitated. The use of high-throughput phenotyping technologies to study large numbers of natural accessions or mutants, or to screen for the effects of large sets of chemicals will allow for further identification of the additional players that constitute the leaf growth regulatory networks. Only a tight co-ordination between these numerous molecular players can support the formation of a functional organ. The connections between the components of the network and their dynamics can be further disentangled through gene-stacking approaches and ultimately through mathematical modelling. In this review, we describe these different approaches that should help to obtain a holistic image of the molecular regulation of organ growth which is of high interest in view of the increasing needs for plant-derived products.