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

The transcription factor HSFA7b controls thermomemory at the shoot apical meristem by regulating ethylene biosynthesis and signaling in Arabidopsis

The shoot apical meristem (SAM) is responsible for overall shoot growth by generating all aboveground structures. Recent research has revealed that the SAM displays an autonomous heat stress (HS) memory of a previous non-lethal HS event. Considering the importance of the SAM for plant growth, it is essential to determine how its thermomemory is mechanistically controlled. Here, we report that HEAT SHOCK TRANSCRIPTION FACTOR A7b (HSFA7b) plays a crucial role in this process in Arabidopsis, as the absence of functional HSFA7b results in the temporal suppression of SAM activity after thermopriming. We found that HSFA7b directly regulates ethylene response at the SAM by binding to the promoter of the key ethylene signaling gene ETHYLENE-INSENSITIVE 3 to establish thermotolerance. Moreover, we demonstrated that HSFA7b regulates the expression of ETHYLENE OVERPRODUCER 1 (ETO1) and ETO1-LIKE 1, both of which encode ethylene biosynthesis repressors, thereby ensuring ethylene homeostasis at the SAM. Taken together, these results reveal a crucial and tissue-specific role for HSFA7b in thermomemory at the Arabidopsis SAM.

Plants distinguish different photoperiods to independently control seasonal flowering and growth

Plants measure daylength (photoperiod) to regulate seasonal growth and flowering. Photoperiodic flowering has been well studied, but less is known about photoperiodic growth. By using a mutant with defects in photoperiodic growth, we identified a seasonal growth regulation pathway that functions in long days in parallel to the canonical long-day photoperiod flowering mechanism. This is achieved by using distinct mechanisms to detect different photoperiods: The flowering pathway measures photoperiod as the duration of light intensity, whereas the growth pathway measures photoperiod as the duration of photosynthetic activity (photosynthetic period). Plants can then independently control expression of genes required for flowering or growth. This demonstrates that seasonal flowering and growth are dissociable, allowing them to be coordinated independently across seasons.

Leaf starch metabolism sets the phase of stomatal rhythm

In leaves of C3 and C4 plants, stomata open during the day to favor CO2 entry for photosynthesis and close at night to prevent inefficient transpiration of water vapor. The circadian clock paces rhythmic stomatal movements throughout the diel (24-h) cycle. Leaf transitory starch is also thought to regulate the diel stomatal movements, yet the underlying mechanisms across time (key moments) and space (relevant leaf tissues) remain elusive. Here, we developed PhenoLeaks, a pipeline to analyze the diel dynamics of transpiration, and used it to screen a series of Arabidopsis (Arabidopsis thaliana) mutants impaired in starch metabolism. We detected a sinusoidal, endogenous rhythm of transpiration that overarches days and nights. We determined that a number of severe mutations in starch metabolism affect the endogenous rhythm through a phase shift, resulting in delayed stomatal movements throughout the daytime and diminished stomatal preopening during the night. Nevertheless, analysis of tissue-specific mutations revealed that neither guard-cell nor mesophyll-cell starch metabolisms are strictly required for normal diel patterns of transpiration. We propose that leaf starch influences the timing of transpiration rhythm through an interplay between the circadian clock and sugars across tissues, while the energetic effect of starch-derived sugars is usually nonlimiting for endogenous stomatal movements.

Non-destructive monitoring method for leaf area of Brassica napus based on image processing and deep learning

Introduction

Leaves are important organs for photosynthesis in plants, and the restriction of leaf growth is among the earliest visible effects under abiotic stress such as nutrient deficiency. Rapidly and accurately monitoring plant leaf area is of great importance in understanding plant growth status in modern agricultural production.

Method

In this paper, an image processing-based non-destructive monitoring device that includes an image acquisition device and image process deep learning net for acquiring Brassica napus (rapeseed) leaf area is proposed. A total of 1,080 rapeseed leaf image areas from five nutrient amendment treatments were continuously collected using the automatic leaf acquisition device and the commonly used area measurement methods (manual and stretching methods).

Results

The average error rate of the manual method is 12.12%, the average error rate of the stretching method is 5.63%, and the average error rate of the splint method is 0.65%. The accuracy of the automatic leaf acquisition device was improved by 11.47% and 4.98% compared with the manual and stretching methods, respectively, and had the advantages of speed and automation. Experiments on the effects of the manual method, stretching method, and splinting method on the growth of rapeseed are conducted, and the growth rate of rapeseed leaves under the stretching method treatment is considerably greater than that of the normal treatment rapeseed.

Discussion

The growth rate of leaves under the splinting method treatment was less than that of the normal rapeseed treatment. The mean intersection over union (mIoU) of the UNet-Attention model reached 90%, and the splint method had higher prediction accuracy with little influence on rapeseed.

Point mutations in the catalytic domain disrupt cellulose synthase (CESA6) vesicle trafficking and protein dynamics

Cellulose, the main component of the plant cell wall, is synthesized by the multimeric cellulose synthase (CESA) complex (CSC). In plant cells, CSCs are assembled in the endoplasmic reticulum or Golgi and transported through the endomembrane system to the plasma membrane (PM). However, how CESA catalytic activity or conserved motifs around the catalytic core influence vesicle trafficking or protein dynamics is not well understood. Here, we used yellow fluorescent protein (YFP)-tagged AtCESA6 and created 18 mutants in key motifs of the catalytic domain to analyze how they affected seedling growth, cellulose biosynthesis, complex formation, and CSC dynamics and trafficking in Arabidopsis thaliana. Seedling growth and cellulose content were reduced by nearly all mutations. Moreover, mutations in most conserved motifs slowed CSC movement in the PM as well as delivery of CSCs to the PM. Interestingly, mutations in the DDG and QXXRW motifs affected YFP-CESA6 abundance in the Golgi. These mutations also perturbed post-Golgi trafficking of CSCs. The 18 mutations were divided into 2 groups based on their phenotypes; we propose that Group I mutations cause CSC trafficking defects, whereas Group II mutations, especially in the QXXRW motif, affect protein folding and/or CSC rosette formation. Collectively, our results demonstrate that the CESA6 catalytic domain is essential for cellulose biosynthesis as well as CSC formation, protein folding and dynamics, and vesicle trafficking.

How to make sense of 3D representations for plant phenotyping: a compendium of processing and analysis techniques

Computer vision technology is moving more and more towards a three-dimensional approach, and plant phenotyping is following this trend. However, despite its potential, the complexity of the analysis of 3D representations has been the main bottleneck hindering the wider deployment of 3D plant phenotyping. In this review we provide an overview of typical steps for the processing and analysis of 3D representations of plants, to offer potential users of 3D phenotyping a first gateway into its application, and to stimulate its further development. We focus on plant phenotyping applications where the goal is to measure characteristics of single plants or crop canopies on a small scale in research settings, as opposed to large scale crop monitoring in the field.

Growing at the right time: interconnecting the TOR pathway with photoperiod and circadian regulation

Plants can adjust their growth to specific times of the day and season. Different photoperiods result in distinct growth patterns, which correlate with specific carbon-partitioning strategies in source (leaves) and sink (roots) organs. Therefore, external cues such as light, day length, and temperature need to be integrated with intracellular processes controlling overall carbon availability and anabolism. The target of rapamycin (TOR) pathway is a signalling hub where environmental signals, circadian information, and metabolic processes converge to regulate plant growth. TOR complex mutants display altered patterns of root growth and starch levels. Moreover, depletion of TOR or reduction in cellular energy levels affect the pace of the clock by extending the period length, suggesting that this pathway could participate in circadian metabolic entrainment. However, this seems to be a mutual interaction, since the TOR pathway components are also under circadian regulation. These results strengthen the role of this signalling pathway as a master sensor of metabolic status, integrating day length and circadian cues to control anabolic processes in the cell, thus promoting plant growth and development. Expanding this knowledge from Arabidopsis thaliana to crops will improve our understanding of the molecular links connecting environmental perception and growth regulation under field conditions.

Low-voltage driving high-resistance liquid crystal micro-lens with electrically tunable depth of field for the light field imaging system

Light field imaging (LFI) based on Liquid crystal microlens array (LC MLAs) are emerging as a significant area for 3D imaging technology in the field of upcoming Internet of things and artificial intelligence era. However, in scenes of LFI through conventional MLAs, such as biological imaging and medicine imaging, the quality of imaging reconstruction will be severely reduced due to the limited depth of field. Here, we are proposed a low-voltage driving LC MLAs with electrically tunable depth of field (DOF) for the LFI system. An aluminum-doped zinc oxide (AZO) film was deposited on the top of the hole-patterned driven-electrode arrays and used as a high resistance (Hi-R) layer, a uniform gradient electric field was obtained across the sandwiched LC cell. Experimental results confirm that the proposed LC MLAs possess high-quality interference rings and tunable focal length at a lower working voltage. In addition, the focal lengths are tunable from 3.93 to 2.62 mm and the DOF are adjustable from 15.60 to 1.23 mm. The experiments demonstrated that the LFI system based on the proposed structure can clearly capture 3D information of the insets with enlarged depths by changing the working voltage and driving frequency, which indicates that the tunable DOF LC MLAs have a potential application prospects for the biological and medical imaging.

Plant mitochondrial FMT and its mammalian homolog CLUH controls development and behavior in Arabidopsis and locomotion in mice

Mitochondria in animals are associated with development, as well as physiological and pathological behaviors. Several conserved mitochondrial genes exist between plants and higher eukaryotes. Yet, the similarities in mitochondrial function between plant and animal species is poorly understood. Here, we show that FMT (FRIENDLY MITOCHONDRIA) from Arabidopsis thaliana, a highly conserved homolog of the mammalian CLUH (CLUSTERED MITOCHONDRIA) gene family encoding mitochondrial proteins associated with developmental alterations and adult physiological and pathological behaviors, affects whole plant morphology and development under both stressed and normal growth conditions. FMT was found to regulate mitochondrial morphology and dynamics, germination, and flowering time. It also affects leaf expansion growth, salt stress responses and hyponastic behavior, including changes in speed of hyponastic movements. Strikingly, Cluh± heterozygous knockout mice also displayed altered locomotive movements, traveling for shorter distances and had slower average and maximum speeds in the open field test. These observations indicate that homologous mitochondrial genes may play similar roles and affect homologous functions in both plants and animals.

Fast estimation of plant growth dynamics using deep neural networks

BACKGROUND

In recent years, there has been an increase of interest in plant behaviour as represented by growth-driven responses. These are generally classified into nastic (internally driven) and tropic (environmentally driven) movements. Nastic movements include circumnutations, a circular movement of plant organs commonly associated with search and exploration, while tropisms refer to the directed growth of plant organs toward or away from environmental stimuli, such as light and gravity. Tracking these movements is therefore fundamental for the study of plant behaviour. Convolutional neural networks, as used for human and animal pose estimation, offer an interesting avenue for plant tracking. Here we adopted the Social LEAP Estimates Animal Poses (SLEAP) framework for plant tracking. We evaluated it on time-lapse videos of cases spanning a variety of parameters, such as: (i) organ types and imaging angles (e.g., top-view crown leaves vs. side-view shoots and roots), (ii) lighting conditions (full spectrum vs. IR), (iii) plant morphologies and scales (100 μm-scale Arabidopsis seedlings vs. cm-scale sunflowers and beans), and (iv) movement types (circumnutations, tropisms and twining).

RESULTS

Overall, we found SLEAP to be accurate in tracking side views of shoots and roots, requiring only a low number of user-labelled frames for training. Top views of plant crowns made up of multiple leaves were found to be more challenging, due to the changing 2D morphology of leaves, and the occlusions of overlapping leaves. This required a larger number of labelled frames, and the choice of labelling "skeleton" had great impact on prediction accuracy, i.e., a more complex skeleton with fewer individuals (tracking individual plants) provided better results than a simpler skeleton with more individuals (tracking individual leaves).

CONCLUSIONS

In all, these results suggest SLEAP is a robust and versatile tool for high-throughput automated tracking of plants, presenting a new avenue for research focusing on plant dynamics.

Genetic architecture of variation in Arabidopsis thaliana rosettes

Rosette morphology across Arabidopsis accessions exhibits considerable variation. Here we report a high-throughput phenotyping approach based on automatic image analysis to quantify rosette shape and dissect the underlying genetic architecture. Shape measurements of the rosettes in a core set of Recombinant Inbred Lines from an advanced mapping population (Multiparent Advanced Generation Inter-Cross or MAGIC) derived from inter-crossing 19 natural accessions. Image acquisition and analysis was scaled to extract geometric descriptors from time stamped images of growing rosettes. Shape analyses revealed heritable morphological variation at early juvenile stages and QTL mapping resulted in over 116 chromosomal regions associated with trait variation within the population. Many QTL linked to variation in shape were located near genes related to hormonal signalling and signal transduction pathways while others are involved in shade avoidance and transition to flowering. Our results suggest rosette shape arises from modular integration of sub-organ morphologies and can be considered a functional trait subjected to selective pressures of subsequent morphological traits. On an applied aspect, QTLs found will be candidates for further research on plant architecture.

Shoot and root single cell sequencing reveals tissue- and daytime-specific transcriptome profiles

Although several large-scale single-cell RNA sequencing (scRNAseq) studies addressing the root of Arabidopsis (Arabidopsis thaliana) have been published, there is still need for a de novo reference map for both root and especially above-ground cell types. As the plants' transcriptome substantially changes throughout the day, shaped by the circadian clock, we performed scRNAseq on both Arabidopsis root and above-ground tissues at defined times of the day. For the root scRNAseq analysis, we used tissue-specific reporter lines grown on plates and harvested at the end of the day (ED). In addition, we submitted above-ground tissues from plants grown on soil at ED and end of the night to scRNAseq, which allowed us to identify common cell types/markers between root and shoot and uncover transcriptome changes to above-ground tissues depending on the time of the day. The dataset was also exploited beyond the traditional scRNAseq analysis to investigate non-annotated and di-cistronic transcripts. We experimentally confirmed the predicted presence of some of these transcripts and also addressed the potential function of a previously unidentified marker gene for dividing cells. In summary, this work provides insights into the spatial control of gene expression from nearly 70,000 cells of Arabidopsis for below- and whole above-ground tissue at single-cell resolution at defined time points.

Four-Dimensional Plant Phenotyping Model Integrating Low-Density LiDAR Data and Multispectral Images

High-throughput platforms for plant phenotyping usually demand expensive high-density LiDAR devices with computational intense methods for characterizing several morphological variables. In fact, most platforms require offline processing to achieve a comprehensive plant architecture model. In this paper, we propose a low-cost plant phenotyping system based on the sensory fusion of low-density LiDAR data with multispectral imagery. Our contribution is twofold: (i) an integrated phenotyping platform with embedded processing methods capable of providing real-time morphological data, and (ii) a multi-sensor fusion algorithm that precisely match the 3D LiDAR point-cloud data with the corresponding multispectral information, aiming for the consolidation of four-dimensional plant models. We conducted extensive experimental tests over two plants with different morphological structures, demonstrating the potential of the proposed solution for enabling real-time plant architecture modeling in the field, based on low-density LiDARs.

Plant growth: the What, the How, and the Why

Growth is a widely used term in plant science and ecology, but it can have different meanings depending on the context and the spatiotemporal scale of analysis. At the meristem level, growth is associated with the production of cells and initiation of new organs. At the organ or plant scale and over short time periods, growth is often used synonymously with tissue expansion, while over longer time periods the increase in biomass is a common metric. At even larger temporal and spatial scales, growth is mostly described as net primary production. Here, we first address the question 'what is growth?'. We propose a general framework to distinguish between the different facets of growth, and the corresponding physiological processes, environmental drivers and mathematical formalisms. Based on these different definitions, we then review how plant growth can be measured and analysed at different organisational, spatial and temporal scales. We conclude by discussing why gaining a better understanding of the different facets of plant growth is essential to disentangle genetic and environmental effects on the phenotype, and to uncover the causalities around source or sink limitations of plant growth.

Primary carbohydrate metabolism genes participate in heat-stress memory at the shoot apical meristem of Arabidopsis thaliana

In plants, the shoot apical meristem (SAM) is essential for the growth of aboveground organs. However, little is known about its molecular responses to abiotic stresses. Here, we show that the SAM of Arabidopsis thaliana displays an autonomous heat-stress (HS) memory of a previous non-lethal HS, allowing the SAM to regain growth after exposure to an otherwise lethal HS several days later. Using RNA sequencing, we identified genes participating in establishing the SAM's HS transcriptional memory, including the stem cell (SC) regulators CLAVATA1 (CLV1) and CLV3, HEAT SHOCK PROTEIN 17.6A (HSP17.6A), and the primary carbohydrate metabolism gene FRUCTOSE-BISPHOSPHATE ALDOLASE 6 (FBA6). We demonstrate that sugar availability is essential for survival of plants at high temperature. HEAT SHOCK TRANSCRIPTION FACTOR A2 (HSFA2A) directly regulates the expression of HSP17.6A and FBA6 by binding to the heat-shock elements in their promoters, indicating that HSFA2 is required for transcriptional activation of SAM memory genes. Collectively, these findings indicate that plants have evolved a sophisticated protection mechanism to maintain SCs and, hence, their capacity to re-initiate shoot growth after stress release.

Robotic Assay for Drought (RoAD): an automated phenotyping system for brassinosteroid and drought responses

Brassinosteroids (BRs) are a group of plant steroid hormones involved in regulating growth, development, and stress responses. Many components of the BR pathway have previously been identified and characterized. However, BR phenotyping experiments are typically performed in a low-throughput manner, such as on Petri plates. Additionally, the BR pathway affects drought responses, but drought experiments are time consuming and difficult to control. To mitigate these issues and increase throughput, we developed the Robotic Assay for Drought (RoAD) system to perform BR and drought response experiments in soil-grown Arabidopsis plants. RoAD is equipped with a robotic arm, a rover, a bench scale, a precisely controlled watering system, an RGB camera, and a laser profilometer. It performs daily weighing, watering, and imaging tasks and is capable of administering BR response assays by watering plants with Propiconazole (PCZ), a BR biosynthesis inhibitor. We developed image processing algorithms for both plant segmentation and phenotypic trait extraction to accurately measure traits including plant area, plant volume, leaf length, and leaf width. We then applied machine learning algorithms that utilize the extracted phenotypic parameters to identify image-derived traits that can distinguish control, drought-treated, and PCZ-treated plants. We carried out PCZ and drought experiments on a set of BR mutants and Arabidopsis accessions with altered BR responses. Finally, we extended the RoAD assays to perform BR response assays using PCZ in Zea mays (maize) plants. This study establishes an automated and non-invasive robotic imaging system as a tool to accurately measure morphological and growth-related traits of Arabidopsis and maize plants in 3D, providing insights into the BR-mediated control of plant growth and stress responses.

Automatic Measurement of Morphological Traits of Typical Leaf Samples

It is still a challenging task to automatically measure plants. A novel method for automatic plant measurement based on a hand-held three-dimensional (3D) laser scanner is proposed. The objective of this method is to automatically select typical leaf samples and estimate their morphological traits from different occluded live plants. The method mainly includes data acquisition and processing. Data acquisition is to obtain the high-precision 3D mesh model of the plant that is reconstructed in real-time during data scanning by a hand-held 3D laser scanner (ZGScan 717, made in Zhongguan Automation Technology, Wuhan, China). Data processing mainly includes typical leaf sample extraction and morphological trait estimation based on a multi-level region growing segmentation method using two leaf shape models. Four scale-related traits and six corresponding scale-invariant traits can be automatically estimated. Experiments on four groups of different canopy-occluded plants are conducted. Experiment results show that for plants with different canopy occlusions, 94.02% of typical leaf samples can be scanned well and 87.61% of typical leaf samples can be automatically extracted. The automatically estimated morphological traits are correlated with the manually measured values EF (the modeling efficiency) above 0.8919 for scale-related traits and EF above 0.7434 for scale-invariant traits). It takes an average of 196.37 seconds (186.08 seconds for data scanning, 5.95 seconds for 3D plant model output, and 4.36 seconds for data processing) for a plant measurement. The robustness and low time cost of the proposed method for different canopy-occluded plants show potential applications for real-time plant measurement and high-throughput plant phenotype.

High-Identical Numerical Aperture, Multifocal Microlens Array through Single-Step Multi-Sized Hole Patterning Photolithography

Imaging applications based on microlens arrays (MLAs) have a great potential for the depth sensor, wide field-of-view camera and the reconstructed hologram. However, the narrow depth-of-field remains the challenge for accurate, reliable depth estimation. Multifocal microlens array (Mf-MLAs) is perceived as a major breakthrough, but existing fabrication methods are still hindered by the expensive, low-throughput, and dissimilar numerical aperture (NA) of individual lenses due to the multiple steps in the photolithography process. This paper reports the fabrication method of high NA, Mf-MLAs for the extended depth-of-field using single-step photolithography assisted by chemical wet etching. The various lens parameters of Mf-MLAs are manipulated by the multi-sized hole photomask and the wet etch time. Theoretical and experimental results show that the Mf-MLAs have three types of lens with different focal lengths, while maintaining the uniform and high NA irrespective of the lens type. Additionally, we demonstrate the multi-focal plane image acquisition via Mf-MLAs integrated into a microscope.

Analysing the phenotype development of soybean plants using low-cost 3D reconstruction

With the development of digital agriculture, 3D reconstruction technology has been widely used to analyse crop phenotypes. To date, most research on 3D reconstruction of field crops has been limited to analysis of population characteristics. Therefore, in this study, we propose a method based on low-cost 3D reconstruction technology to analyse the phenotype development during the whole growth period. Based on the phenotypic parameters extracted from the 3D reconstruction model, we identified the "phenotypic fingerprint" of the relevant phenotypes throughout the whole growth period of soybean plants and completed analysis of the plant growth patterns using a logistic growth model. The phenotypic fingerprint showed that, before the R3 period, the growth of the five varieties was similar. After the R5 period, the differences among the five cultivars gradually increased. This result indicates that the phenotypic fingerprint can accurately reveal the patterns of phenotypic changes. The logistic growth model of soybean plants revealed the time points of maximum growth rate of the five soybean varieties, and this information can provide a basis for developing guidelines for water and fertiliser application to crops. These findings will provide effective guidance for breeding and field management of soybean and other 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.

m5C Methylation Guides Systemic Transport of Messenger RNA over Graft Junctions in Plants

In plants, transcripts move to distant body parts to potentially act as systemic signals regulating development and growth. Thousands of messenger RNAs (mRNAs) are transported across graft junctions via the phloem to distinct plant parts. Little is known regarding features, structural motifs, and potential base modifications of transported transcripts and how these may affect their mobility. We identified Arabidopsis thaliana mRNAs harboring the modified base 5-methylcytosine (m⁵C) and found that these are significantly enriched in mRNAs previously described as mobile, moving over graft junctions to distinct plant parts. We confirm this finding with graft-mobile methylated mRNAs TRANSLATIONALLY CONTROLLED TUMOR PROTEIN 1 (TCTP1) and HEAT SHOCK COGNATE PROTEIN 70.1 (HSC70.1), whose mRNA transport is diminished in mutants deficient in m⁵C mRNA methylation. Together, our results point toward an essential role of cytosine methylation in systemic mRNA mobility in plants and that TCTP1 mRNA mobility is required for its signaling function.

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.

Large area fabrication of engineered microlens array with low sag height for light-field imaging

Recently, intensive research on microlens array (MLA) was undertaken, which involved intertwining with the light-field imaging in order to obtain four-dimensional information. Although several fabrication approaches for MLA attempted to achieve high density and precision geometry, further progress is necessary. In this study, we present a cost-effective fabrication strategy for geometrically tunable polymer MLA with extremely low sag height (~3 μm) through improved MEMS wet etching process. Additionally, we assemble a hand-crafted light-field camera by integrating the elaborately customized MLA with a commercial digital camera. Finally, we demonstrate representative light-field imaging features including refocusing and all-in focusing image from a single exposure.

Multiple circadian clock outputs regulate diel turnover of carbon and nitrogen reserves

Plants accumulate reserves in the daytime to support growth at night. Circadian regulation of diel reserve turnover was investigated by profiling starch, sugars, glucose 6-phosphate, organic acids, and amino acids during a light-dark cycle and after transfer to continuous light in Arabidopsis wild types and in mutants lacking dawn (lhy cca1), morning (prr7 prr9), dusk (toc1, gi), or evening (elf3) clock components. The metabolite time series were integrated with published time series for circadian clock transcripts to identify circadian outputs that regulate central metabolism. (a) Starch accumulation was slower in elf3 and prr7 prr9. It is proposed that ELF3 positively regulates starch accumulation. (b) Reducing sugars were high early in the T-cycle in elf3, revealing that ELF3 negatively regulates sucrose recycling. (c) The pattern of starch mobilization was modified in all five mutants. A model is proposed in which dawn and dusk/evening components interact to pace degradation to anticipated dawn. (d) An endogenous oscillation of glucose 6-phosphate revealed that the clock buffers metabolism against the large influx of carbon from photosynthesis. (e) Low levels of organic and amino acids in lhy cca1 and high levels in prr7 prr9 provide evidence that the dawn components positively regulate the accumulation of amino acid reserves.

Pheno-Deep Counter: a unified and versatile deep learning architecture for leaf counting

Direct observation of morphological plant traits is tedious and a bottleneck for high-throughput phenotyping. Hence, interest in image-based analysis is increasing, with the requirement for software that can reliably extract plant traits, such as leaf count, preferably across a variety of species and growth conditions. However, current leaf counting methods do not work across species or conditions and therefore may lack broad utility. In this paper, we present Pheno-Deep Counter, a single deep network that can predict leaf count in two-dimensional (2D) plant images of different species with a rosette-shaped appearance. We demonstrate that our architecture can count leaves from multi-modal 2D images, such as visible light, fluorescence and near-infrared. Our network design is flexible, allowing for inputs to be added or removed to accommodate new modalities. Furthermore, our architecture can be used as is without requiring dataset-specific customization of the internal structure of the network, opening its use to new scenarios. Pheno-Deep Counter is able to produce accurate predictions in many plant species and, once trained, can count leaves in a few seconds. Through our universal and open source approach to deep counting we aim to broaden utilization of machine learning-based approaches to leaf counting. Our implementation can be downloaded at https://bitbucket.org/tuttoweb/pheno-deep-counter.

Response of Arabidopsis primary metabolism and circadian clock to low night temperature in a natural light environment

Plants are exposed to varying irradiance and temperature within a day and from day to day. We previously investigated metabolism in a temperature-controlled greenhouse at the spring equinox on both a cloudy and a sunny day [daily light integral (DLI) of 7 mol m-2 d-1 and 12 mol m-2 d-1]. Diel metabolite profiles were largely captured in sinusoidal simulations at similar DLIs in controlled-environment chambers, except that amino acids were lower in natural light regimes. We now extend the DLI12 study by investigating metabolism in a natural light regime with variable temperature including cool nights. Starch was not completely turned over, anthocyanins and proline accumulated, and protein content rose. Instead of decreasing, amino acid content rose. Connectivity in central metabolism, which decreased in variable light, was not further weakened by variable temperature. We propose that diel metabolism operates better when light and temperature are co-varying. We also compared transcript abundance of 10 circadian clock genes in this temperature-variable regime with the temperature-controlled natural and sinusoidal light regimes. Despite temperature compensation, peak timing and abundance for dawn- and day-phased genes and GIGANTEA were slightly modified in the variable temperature treatment. This may delay dawn clock activity until the temperature rises enough to support rapid metabolism and photosynthesis.

Arabidopsis phenotyping through geometric morphometrics

Background

Recently, great technical progress has been achieved in the field of plant phenotyping. High-throughput platforms and the development of improved algorithms for rosette image segmentation make it possible to extract shape and size parameters for genetic, physiological, and environmental studies on a large scale. The development of low-cost phenotyping platforms and freeware resources make it possible to widely expand phenotypic analysis tools for Arabidopsis. However, objective descriptors of shape parameters that could be used independently of the platform and segmentation software used are still lacking, and shape descriptions still rely on ad hoc or even contradictory descriptors, which could make comparisons difficult and perhaps inaccurate. Modern geometric morphometrics is a family of methods in quantitative biology proposed to be the main source of data and analytical tools in the emerging field of phenomics studies. Based on the location of landmarks (corresponding points) over imaged specimens and by combining geometry, multivariate analysis, and powerful statistical techniques, these tools offer the possibility to reproducibly and accurately account for shape variations among groups and measure them in shape distance units.

Results

Here, a particular scheme of landmark placement on Arabidopsis rosette images is proposed to study shape variation in viral infection processes. Shape differences between controls and infected plants are quantified throughout the infectious process and visualized. Quantitative comparisons between two unrelated ssRNA+ viruses are shown, and reproducibility issues are assessed.

Conclusions

Combined with the newest automated platforms and plant segmentation procedures, geometric morphometric tools could boost phenotypic features extraction and processing in an objective, reproducible manner.

Diurnal periodicity of assimilate transport shapes resource allocation and whole-plant carbon balance

Whole-plant carbon balance comprises diurnal fluctuations of photosynthetic carbon gain and respiratory losses, as well as partitioning of assimilates between phototrophic and heterotrophic organs. Because it is difficult to access, the root system is frequently neglected in growth models, or its metabolism is rated based on generalizations from other organs. Here, whole-plant cuvettes were used for investigating total-plant carbon exchange with the environment over full diurnal cycles. Dynamics of primary metabolism and diurnally resolved phloem exudation profiles, as proxy of assimilate transport, were combined to obtain a full picture of resource allocation. This uncovered a strong impact of periodicity of inter-organ transport on the efficiency of carbon gain. While a sinusoidal fluctuation of the transport rate, with minor diel deflections, minimized respiratory losses in Arabidopsis wild-type plants, triangular or rectangular patterns of transport, found in mutants defective in either starch or sucrose metabolism, increased root respiration at the end or beginning of the day, respectively. Power spectral density and cross-correlation analysis revealed that only the rate of starch synthesis was strictly correlated to the rate of net photosynthesis in wild-type, while in a sucrose-phosphate synthase mutant (spsa1), this applied also to carboxylate synthesis, serving as an alternative carbon pool. In the starchless mutant of plastidial phospho-gluco mutase (pgm), none of these rates, but concentrations of sucrose and glucose in the root, followed the pattern of photosynthesis, indicating direct transduction of shoot sugar levels to the root. The results demonstrate that starch metabolism alone is insufficient to buffer diurnal fluctuations of carbon exchange.

Automatic Non-Destructive Growth Measurement of Leafy Vegetables Based on Kinect

Non-destructive plant growth measurement is essential for plant growth and health research. As a 3D sensor, Kinect v2 has huge potentials in agriculture applications, benefited from its low price and strong robustness. The paper proposes a Kinect-based automatic system for non-destructive growth measurement of leafy vegetables. The system used a turntable to acquire multi-view point clouds of the measured plant. Then a series of suitable algorithms were applied to obtain a fine 3D reconstruction for the plant, while measuring the key growth parameters including relative/absolute height, total/projected leaf area and volume. In experiment, 63 pots of lettuce in different growth stages were measured. The result shows that the Kinect-measured height and projected area have fine linear relationship with reference measurements. While the measured total area and volume both follow power law distributions with reference data. All these data have shown good fitting goodness (R² = 0.9457–0.9914). In the study of biomass correlations, the Kinect-measured volume was found to have a good power law relationship (R² = 0.9281) with fresh weight. In addition, the system practicality was validated by performance and robustness analysis.

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).

Carbon Supply and the Regulation of Cell Wall Synthesis

All plant cells are surrounded by a cell wall that determines the directionality of cell growth and protects the cell against its environment. Plant cell walls are comprised primarily of polysaccharides and represent the largest sink for photosynthetically fixed carbon, both for individual plants and in the terrestrial biosphere as a whole. Cell wall synthesis is a highly sophisticated process, involving multiple enzymes and metabolic intermediates, intracellular trafficking of proteins and cell wall precursors, assembly of cell wall polymers into the extracellular matrix, remodeling of polymers and their interactions, and recycling of cell wall sugars. In this review we discuss how newly fixed carbon, in the form of UDP-glucose and other nucleotide sugars, contributes to the synthesis of cell wall polysaccharides, and how cell wall synthesis is influenced by the carbon status of the plant, with a focus on the model species Arabidopsis (Arabidopsis thaliana).

A PLA-iRoCS Pipeline for the Localization of Protein-Protein Interactions In Situ

In plants as well as other organisms, protein localization alone is insufficient to provide a mechanistic link between stimulus and process regulation. This is because protein-protein interactions are central to the regulation of biological processes. However, they remain very difficult to detect in situ, with the choice of tools for the detection of protein-protein interaction in situ still in need of expansion. Here, we provide a protocol for the detection and accurate localization of protein interactions based on the combination of a whole-mount proximity ligation assay and iRoCS, a coordinate system able to standardize subtle differences between the architecture of individual Arabidopsis roots.

Leaf-GP: an open and automated software application for measuring growth phenotypes for arabidopsis and wheat

BACKGROUND

Plants demonstrate dynamic growth phenotypes that are determined by genetic and environmental factors. Phenotypic analysis of growth features over time is a key approach to understand how plants interact with environmental change as well as respond to different treatments. Although the importance of measuring dynamic growth traits is widely recognised, available open software tools are limited in terms of batch image processing, multiple traits analyses, software usability and cross-referencing results between experiments, making automated phenotypic analysis problematic.

RESULTS

Here, we present Leaf-GP (Growth Phenotypes), an easy-to-use and open software application that can be executed on different computing platforms. To facilitate diverse scientific communities, we provide three software versions, including a graphic user interface (GUI) for personal computer (PC) users, a command-line interface for high-performance computer (HPC) users, and a well-commented interactive Jupyter Notebook (also known as the iPython Notebook) for computational biologists and computer scientists. The software is capable of extracting multiple growth traits automatically from large image datasets. We have utilised it in Arabidopsis thaliana and wheat (Triticum aestivum) growth studies at the Norwich Research Park (NRP, UK). By quantifying a number of growth phenotypes over time, we have identified diverse plant growth patterns between different genotypes under several experimental conditions. As Leaf-GP has been evaluated with noisy image series acquired by different imaging devices (e.g. smartphones and digital cameras) and still produced reliable biological outputs, we therefore believe that our automated analysis workflow and customised computer vision based feature extraction software implementation can facilitate a broader plant research community for their growth and development studies. Furthermore, because we implemented Leaf-GP based on open Python-based computer vision, image analysis and machine learning libraries, we believe that our software not only can contribute to biological research, but also demonstrates how to utilise existing open numeric and scientific libraries (e.g. Scikit-image, OpenCV, SciPy and Scikit-learn) to build sound plant phenomics analytic solutions, in a efficient and effective way.

CONCLUSIONS

Leaf-GP is a sophisticated software application that provides three approaches to quantify growth phenotypes from large image series. We demonstrate its usefulness and high accuracy based on two biological applications: (1) the quantification of growth traits for Arabidopsis genotypes under two temperature conditions; and (2) measuring wheat growth in the glasshouse over time. The software is easy-to-use and cross-platform, which can be executed on Mac OS, Windows and HPC, with open Python-based scientific libraries preinstalled. Our work presents the advancement of how to integrate computer vision, image analysis, machine learning and software engineering in plant phenomics software implementation. To serve the plant research community, our modulated source code, detailed comments, executables (.exe for Windows; .app for Mac), and experimental results are freely available at https://github.com/Crop-Phenomics-Group/Leaf-GP/releases.

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.

Dancing in the dark: darkness as a signal in plants

Daily cycles of light and dark provide an organizing principle and temporal constraints under which life on Earth evolved. While light is often the focus of plant studies, it is only half the story. Plants continuously adjust to their surroundings, taking both dawn and dusk as cues to organize their growth, development and metabolism to appropriate times of day. In this review, we examine the effects of darkness on plant physiology and growth. We describe the similarities and differences between seedlings grown in the dark versus those grown in light-dark cycles, and the evolution of etiolated growth. We discuss the integration of the circadian clock into other processes, looking carefully at the points of contact between clock genes and growth-promoting gene-regulatory networks in temporal gating of growth. We also examine daily starch accumulation and degradation, and the possible contribution of dark-specific metabolic controls in regulating energy and growth. Examining these studies together reveals a complex and continuous balancing act, with many signals, dark included, contributing information and guiding the plant through its life cycle. The extraordinary interconnection between light and dark is manifest during cycles of day and night and during seedling emergence above versus below the soil surface.

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.

Growth rate correlates negatively with protein turnover in Arabidopsis accessions

Previous studies with Arabidopsis accessions revealed that biomass correlates negatively to dusk starch content and total protein, and positively to the maximum activities of enzymes in photosynthesis. We hypothesized that large accessions have lower ribosome abundance and lower rates of protein synthesis, and that this is compensated by lower rates of protein degradation. This would increase growth efficiency and allow more investment in photosynthetic machinery. We analysed ribosome abundance and polysome loading in 19 accessions, modelled the rates of protein synthesis and compared them with the observed rate of growth. Large accessions contained less ribosomes than small accessions, due mainly to cytosolic ribosome abundance falling at night in large accessions. The modelled rates of protein synthesis resembled those required for growth in large accessions, but were up to 30% in excess in small accessions. We then employed ¹³ CO₂ pulse-chase labelling to measure the rates of protein synthesis and degradation in 13 accessions. Small accessions had a slightly higher rate of protein synthesis and much higher rates of protein degradation than large accessions. Protein turnover was negligible in large accessions but equivalent to up to 30% of synthesised protein day^-1 in small accessions. We discuss to what extent the decrease in growth in small accessions can be quantitatively explained by known costs of protein turnover and what factors may lead to the altered diurnal dynamics and increase of ribosome abundance in small accessions, and propose that there is a trade-off between protein turnover and maximisation of growth rate.

Phenotiki: an open software and hardware platform for affordable and easy image-based phenotyping of rosette-shaped plants

Phenotyping is important to understand plant biology, but current solutions are costly, not versatile or are difficult to deploy. To solve this problem, we present Phenotiki, an affordable system for plant phenotyping that, relying on off-the-shelf parts, provides an easy to install and maintain platform, offering an out-of-box experience for a well-established phenotyping need: imaging rosette-shaped plants. The accompanying software (with available source code) processes data originating from our device seamlessly and automatically. Our software relies on machine learning to devise robust algorithms, and includes an automated leaf count obtained from 2D images without the need of depth (3D). Our affordable device (~€200) can be deployed in growth chambers or greenhouse to acquire optical 2D images of approximately up to 60 adult Arabidopsis rosettes concurrently. Data from the device are processed remotely on a workstation or via a cloud application (based on CyVerse). In this paper, we present a proof-of-concept validation experiment on top-view images of 24 Arabidopsis plants in a combination of genotypes that has not been compared previously. Phenotypic analysis with respect to morphology, growth, color and leaf count has not been performed comprehensively before now. We confirm the findings of others on some of the extracted traits, showing that we can phenotype at reduced cost. We also perform extensive validations with external measurements and with higher fidelity equipment, and find no loss in statistical accuracy when we use the affordable setting that we propose. Device set-up instructions and analysis software are publicly available ( http://phenotiki.com).

rosettR: protocol and software for seedling area and growth analysis

BACKGROUND

Growth is an important parameter to consider when studying the impact of treatments or mutations on plant physiology. Leaf area and growth rates can be estimated efficiently from images of plants, but the experiment setup, image analysis, and statistical evaluation can be laborious, often requiring substantial manual effort and programming skills.

RESULTS

Here we present rosettR, a non-destructive and high-throughput phenotyping protocol for the measurement of total rosette area of seedlings grown in plates in sterile conditions. We demonstrate that our protocol can be used to accurately detect growth differences among different genotypes and in response to light regimes and osmotic stress. rosettR is implemented as a package for the statistical computing software R and provides easy to use functions to design an experiment, analyze the images, and generate reports on quality control as well as a final comparison across genotypes and applied treatments. Experiment procedures are included as part of the package documentation.

CONCLUSIONS

Using rosettR it is straight-forward to perform accurate, reproducible measurements of rosette area and relative growth rate with high-throughput using inexpensive equipment. Suitable applications include screening mutant populations for growth phenotypes visible at early growth stages and profiling different genotypes in a wide variety of treatments.

Time of day determines Arabidopsis transcriptome and growth dynamics under mild drought

Drought stress is a major problem for agriculture worldwide, causing significant yield losses. Plants have developed highly flexible mechanisms to deal with drought, including organ- and developmental stage-specific responses. In young leaves, growth is repressed as an active mechanism to save water and energy, increasing the chances of survival but decreasing yield. Despite its importance, the molecular basis for this growth inhibition is largely unknown. Here, we present a novel approach to explore early molecular mechanisms controlling Arabidopsis leaf growth inhibition following mild drought. We found that growth and transcriptome responses to drought are highly dynamic. Growth was only repressed by drought during the day, and our evidence suggests that this may be due to gating by the circadian clock. Similarly, time of day strongly affected the extent, specificity, and in certain cases even direction of drought-induced changes in gene expression. These findings underscore the importance of taking into account diurnal patterns to understand stress responses, as only a small core of drought-responsive genes are affected by drought at all times of the day. Finally, we leveraged our high-resolution data to demonstrate that phenotypic and transcriptome responses can be matched to identify putative novel regulators of growth under mild drought.

Leaf Segmentation and Tracking in Arabidopsis thaliana Combined to an Organ-Scale Plant Model for Genotypic Differentiation

A promising method for characterizing the phenotype of a plant as an interaction between its genotype and its environment is to use refined organ-scale plant growth models that use the observation of architectural traits, such as leaf area, containing a lot of information on the whole history of the functioning of the plant. The Phenoscope, a high-throughput automated platform, allowed the acquisition of zenithal images of Arabidopsis thaliana over twenty one days for 4 different genotypes. A novel image processing algorithm involving both segmentation and tracking of the plant leaves allows to extract areas of the latter. First, all the images in the series are segmented independently using a watershed-based approach. A second step based on ellipsoid-shaped leaves is then applied on the segments found to refine the segmentation. Taking into account all the segments at every time, the whole history of each leaf is reconstructed by choosing recursively through time the most probable segment achieving the best score, computed using some characteristics of the segment such as its orientation, its distance to the plant mass center and its area. These results are compared to manually extracted segments, showing a very good accordance in leaf rank and that they therefore provide low-biased data in large quantity for leaf areas. Such data can therefore be exploited to design an organ-scale plant model adapted from the existing GreenLab model for A. thaliana and subsequently parameterize it. This calibration of the model parameters should pave the way for differentiation between the Arabidopsis genotypes.

Fast High Resolution Volume Carving for 3D Plant Shoot Reconstruction

Volume carving is a well established method for visual hull reconstruction and has been successfully applied in plant phenotyping, especially for 3d reconstruction of small plants and seeds. When imaging larger plants at still relatively high spatial resolution (≤1 mm), well known implementations become slow or have prohibitively large memory needs. Here we present and evaluate a computationally efficient algorithm for volume carving, allowing e.g., 3D reconstruction of plant shoots. It combines a well-known multi-grid representation called "Octree" with an efficient image region integration scheme called "Integral image." Speedup with respect to less efficient octree implementations is about 2 orders of magnitude, due to the introduced refinement strategy "Mark and refine." Speedup is about a factor 1.6 compared to a highly optimized GPU implementation using equidistant voxel grids, even without using any parallelization. We demonstrate the application of this method for trait derivation of banana and maize plants.

Fast High Resolution Volume Carving for 3D Plant Shoot Reconstruction

Volume carving is a well established method for visual hull reconstruction and has been successfully applied in plant phenotyping, especially for 3d reconstruction of small plants and seeds. When imaging larger plants at still relatively high spatial resolution (≤1 mm), well known implementations become slow or have prohibitively large memory needs. Here we present and evaluate a computationally efficient algorithm for volume carving, allowing e.g., 3D reconstruction of plant shoots. It combines a well-known multi-grid representation called "Octree" with an efficient image region integration scheme called "Integral image." Speedup with respect to less efficient octree implementations is about 2 orders of magnitude, due to the introduced refinement strategy "Mark and refine." Speedup is about a factor 1.6 compared to a highly optimized GPU implementation using equidistant voxel grids, even without using any parallelization. We demonstrate the application of this method for trait derivation of banana and maize plants.

Non-destructive, high-content analysis of wheat grain traits using X-ray micro computed tomography

BACKGROUND

Wheat is one of the most widely grown crop in temperate climates for food and animal feed. In order to meet the demands of the predicted population increase in an ever-changing climate, wheat production needs to dramatically increase. Spike and grain traits are critical determinants of final yield and grain uniformity a commercially desired trait, but their analysis is laborious and often requires destructive harvest. One of the current challenges is to develop an accurate, non-destructive method for spike and grain trait analysis capable of handling large populations.

RESULTS

In this study we describe the development of a robust method for the accurate extraction and measurement of spike and grain morphometric parameters from images acquired by X-ray micro-computed tomography (μCT). The image analysis pipeline developed automatically identifies plant material of interest in μCT images, performs image analysis, and extracts morphometric data. As a proof of principle, this integrated methodology was used to analyse the spikes from a population of wheat plants subjected to high temperatures under two different water regimes. Temperature has a negative effect on spike height and grain number with the middle of the spike being the most affected region. The data also confirmed that increased grain volume was correlated with the decrease in grain number under mild stress.

CONCLUSIONS

Being able to quickly measure plant phenotypes in a non-destructive manner is crucial to advance our understanding of gene function and the effects of the environment. We report on the development of an image analysis pipeline capable of accurately and reliably extracting spike and grain traits from crops without the loss of positional information. This methodology was applied to the analysis of wheat spikes can be readily applied to other economically important crop species.

Correction model for microlens array assembly error in light field camera

In a light field camera, a microlens array (MLA) assembly error can affect the quality of the image. In this study, aiming to ensure corrective imaging using a light field camera, we accurately evaluate and eliminate the assembly error. We used an error image and a standard image to confirm the MLA assembly error, and we developed an assembly error correction model combined with an image quality evaluation index to correct the error. The proposed error correction model can be employed for various assembly errors and different error ranges. Quantitative analyses are performed for these different scenarios. The proposed model can be applied in accurate imaging using a light field camera, four-dimensional optical radiation field information reconstitution, MLA manufacturing and assembly processes, etc.

Photoperiod-dependent changes in the phase of core clock transcripts and global transcriptional outputs at dawn and dusk in Arabidopsis

Plants use the circadian clock to sense photoperiod length. Seasonal responses like flowering are triggered at a critical photoperiod when a light-sensitive clock output coincides with light or darkness. However, many metabolic processes, like starch turnover, and growth respond progressively to photoperiod duration. We first tested the photoperiod response of 10 core clock genes and two output genes. qRT-PCR analyses of transcript abundance under 6, 8, 12 and 18 h photoperiods revealed 1-4 h earlier peak times under short photoperiods and detailed changes like rising PRR7 expression before dawn. Clock models recapitulated most of these changes. We explored the consequences for global gene expression by performing transcript profiling in 4, 6, 8, 12 and 18 h photoperiods. There were major changes in transcript abundance at dawn, which were as large as those between dawn and dusk in a given photoperiod. Contributing factors included altered timing of the clock relative to dawn, light signalling and changes in carbon availability at night as a result of clock-dependent regulation of starch degradation. Their interaction facilitates coordinated transcriptional regulation of key processes like starch turnover, anthocyanin, flavonoid and glucosinolate biosynthesis and protein synthesis and underpins the response of metabolism and growth to photoperiod.