OSCILLATOR: A system for analysis of diurnal leaf growth using infrared photography combined with wavelet transformation

A low-cost open-source imaging platform reveals spatiotemporal insight into leaf elongation and movement

Plant organs move throughout the diurnal cycle, changing leaf and petiole positions to balance light capture, leaf temperature, and water loss under dynamic environmental conditions. Upward movement of the petiole, called hyponasty, is one of several traits of the shade avoidance syndrome (SAS). SAS traits are elicited upon perception of vegetation shade signals such as far-red light (FR) and improve light capture in dense vegetation. Monitoring plant movement at a high temporal resolution allows studying functionality and molecular regulation of hyponasty. However, high temporal resolution imaging solutions are often very expensive, making this unavailable to many researchers. Here, we present a modular and low-cost imaging setup, based on small Raspberry Pi computers that can track leaf movements and elongation growth with high temporal resolution. We also developed an open-source, semiautomated image analysis pipeline. Using this setup, we followed responses to FR enrichment, light intensity, and their interactions. Tracking both elongation and the angle of the petiole, lamina, and entire leaf in Arabidopsis (Arabidopsis thaliana) revealed insight into R:FR sensitivities of leaf growth and movement dynamics and the interactions of R:FR with background light intensity. The detailed imaging options of this system allowed us to identify spatially separate bending points for petiole and lamina positioning of the leaf.

Automated extraction of pod phenotype data from micro-computed tomography

INTRODUCTION

Plant image datasets have the potential to greatly improve our understanding of the phenotypic response of plants to environmental and genetic factors. However, manual data extraction from such datasets are known to be time-consuming and resource intensive. Therefore, the development of efficient and reliable machine learning methods for extracting phenotype data from plant imagery is crucial.

METHODS

In this paper, a current gold standard computed vision method for detecting and segmenting objects in three-dimensional imagery (StartDist-3D) is applied to X-ray micro-computed tomography scans of oilseed rape (Brassica napus) mature pods.

RESULTS

With a relatively minimal training effort, this fine-tuned StarDist-3D model accurately detected (Validation F1-score = 96.3%,Testing F1-score = 99.3%) and predicted the shape (mean matched score = 90%) of seeds.

DISCUSSION

This method then allowed rapid extraction of data on the number, size, shape, seed spacing and seed location in specific valves that can be integrated into models of plant development or crop yield. Additionally, the fine-tuned StarDist-3D provides an efficient way to create a dataset of segmented images of individual seeds that could be used to further explore the factors affecting seed development, abortion and maturation synchrony within the pod. There is also potential for the fine-tuned Stardist-3D method to be applied to imagery of seeds from other plant species, as well as imagery of similarly shaped plant structures such as beans or wheat grains, provided the structures targeted for detection and segmentation can be described as star-convex polygons.

A digital sensor to measure real-time leaf movements and detect abiotic stress in plants

Plant and plant organ movements are the result of a complex integration of endogenous growth and developmental responses, partially controlled by the circadian clock, and external environmental cues. Monitoring of plant motion is typically done by image-based phenotyping techniques with the aid of computer vision algorithms. Here we present a method to measure leaf movements using a digital inertial measurement unit (IMU) sensor. The lightweight sensor is easily attachable to a leaf or plant organ and records angular traits in real-time for two dimensions (pitch and roll) with high resolution (measured sensor oscillations of 0.36 ± 0.53° for pitch and 0.50 ± 0.65° for roll). We were able to record simple movements such as petiole bending, as well as complex lamina motions, in several crops, ranging from tomato to banana. We also assessed growth responses in terms of lettuce rosette expansion and maize seedling stem movements. The IMU sensors are capable of detecting small changes of nutations (i.e. bending movements) in leaves of different ages and in different plant species. In addition, the sensor system can also monitor stress-induced leaf movements. We observed that unfavorable environmental conditions evoke certain leaf movements, such as drastic epinastic responses, as well as subtle fading of the amplitude of nutations. In summary, the presented digital sensor system enables continuous detection of a variety of leaf motions with high precision, and is a low-cost tool in the field of plant phenotyping, with potential applications in early stress detection.

Chronoculture, harnessing the circadian clock to improve crop yield and sustainability

Human health is dependent on a plentiful and nutritious supply of food, primarily derived from crop plants. Rhythmic supply of light as a result of the day and night cycle led to the evolution of circadian clocks that modulate most plant physiology, photosynthesis, metabolism, and development. To regulate crop traits and adaptation, breeders have indirectly selected for variation at circadian genes. The pervasive impact of the circadian system on crops suggests that future food production might be improved by modifying circadian rhythms, engineering the timing of transgene expression, and applying agricultural treatments at the most effective time of day. We describe the applied research required to take advantage of circadian biology in agriculture to increase production and reduce inputs.

A temperature regime that disrupts clock-controlled starch mobilization induces transient carbohydrate starvation, resulting in compact growth

In nature plants are usually subjected to a light/temperature regime of warm day and cold night (referred to as +DIF). Compared to growth under +DIF, Arabidopsis plants show compact growth under the same photoperiod, but with an inverse temperature regime (cold day and warm night: -DIF). Here we show that -DIF differentially affects the phase and amplitude of core clock gene expression. Under -DIF the phase of the morning clock gene CIRCADIAN CLOCK ASSOCIATED 1 (CCA1) is delayed, similar to that of plants grown on low sucrose. Indeed, under -DIF carbohydrate (CHO) starvation marker genes are specifically upregulated at the End of the Night (EN) in Arabidopsis rosettes. However, only in inner-rosette tissue (small sink leaves and petioles of older leaves) sucrose levels are lower under -DIF compared to under +DIF, suggesting that sucrose in source leaf blades is not sensed for CHO status and that sucrose transport from source to sink may be impaired at EN. CHO-starvation under -DIF correlated with increased starch breakdown during the night and decreased starch accumulation during the day. Moreover, we demonstrate that different ways of inducing CHO-starvation all link to reduced growth of sink leaves. Practical implications for control of plant growth in horticulture are discussed.

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.

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.

Circadian, Carbon, and Light Control of Expansion Growth and Leaf Movement

We used Phytotyping4D to investigate the contribution of clock and light signaling to the diurnal regulation of rosette expansion growth and leaf movement in Arabidopsis (Arabidopsis thaliana). Wild-type plants and clock mutants with a short (lhycca1) and long (prr7prr9) period were analyzed in a T24 cycle and in T-cycles that were closer to the mutants' period. Wild types also were analyzed in various photoperiods and after transfer to free-running light or darkness. Rosette expansion and leaf movement exhibited a circadian oscillation, with superimposed transients after dawn and dusk. Diurnal responses were modified in clock mutants. lhycca1 exhibited an inhibition of growth at the end of night and growth rose earlier after dawn, whereas prr7prr9 showed decreased growth for the first part of the light period. Some features were partly rescued by a matching T-cycle, like the inhibition in lhycca1 at the end of the night, indicating that it is due to premature exhaustion of starch. Other features were not rescued, revealing that the clock also regulates expansion growth more directly. Expansion growth was faster at night than in the daytime, whereas published work has shown that the synthesis of cellular components is faster in the day than at nighttime. This temporal uncoupling became larger in short photoperiods and may reflect the differing dependence of expansion and biosynthesis on energy, carbon, and water. While it has been proposed that leaf expansion and movement are causally linked, we did not observe a consistent temporal relationship between expansion and leaf movement.

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).

Disaggregating polyploidy, parental genome dosage and hybridity contributions to heterosis in Arabidopsis thaliana

Heterosis is the phenomenon whereby hybrid offspring of genetically divergent parents display superior characteristics compared with their parents. Although hybridity and polyploidy can influence heterosis in hybrid plants, the differential contributions of hybridity vs polyploidy to heterosis effects remain unknown. To address this question, we investigated heterosis effects on rosette size and growth rate of 88 distinct F1 lines of Arabidopsis thaliana consisting of diploids, reciprocal triploids and tetraploids in isogenic and hybrid genetic contexts. 'Heterosis without hybridity' effects on plant size can be generated in genetically isogenic F1 triploid plants. Paternal genome excess F1 triploids display positive heterosis, whereas maternal genome excess F1 s display negative heterosis effects. Paternal genome dosage increases plant size in F1 hybrid triploid plants by, on average, 57% (in contrast with 35% increase displayed by F1 diploid hybrids). Such effects probably derive from differential seed size, as the growth rate of triploids was similar to diploids. Tetraploid plants display a lower growth rate compared with other ploidies, whereas hybrids display increased early stage growth rate. By disaggregating heterosis effects caused by hybridity vs genome dosage, we advance our understanding of heterosis in plants and facilitate novel paternal genome dosage-based strategies to enhance heterosis effects in crop plants.

A comparison of high-throughput techniques for assaying circadian rhythms in plants

Over the last two decades, the development of high-throughput techniques has enabled us to probe the plant circadian clock, a key coordinator of vital biological processes, in ways previously impossible. With the circadian clock increasingly implicated in key fitness and signalling pathways, this has opened up new avenues for understanding plant development and signalling. Our tool-kit has been constantly improving through continual development and novel techniques that increase throughput, reduce costs and allow higher resolution on the cellular and subcellular levels. With circadian assays becoming more accessible and relevant than ever to researchers, in this paper we offer a review of the techniques currently available before considering the horizons in circadian investigation at ever higher throughputs and resolutions.

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.

Thermoperiodic control of hypocotyl elongation depends on auxin-induced ethylene signaling that controls downstream PHYTOCHROME INTERACTING FACTOR3 activity

We show that antiphase light-temperature cycles (negative day-night temperature difference [-DIF]) inhibit hypocotyl growth in Arabidopsis (Arabidopsis thaliana). This is caused by reduced cell elongation during the cold photoperiod. Cell elongation in the basal part of the hypocotyl under -DIF was restored by both 1-aminocyclopropane-1-carboxylic acid (ACC; ethylene precursor) and auxin, indicating limited auxin and ethylene signaling under -DIF. Both auxin biosynthesis and auxin signaling were reduced during -DIF. In addition, expression of several ACC Synthase was reduced under -DIF but could be restored by auxin application. In contrast, the reduced hypocotyl elongation of ethylene biosynthesis and signaling mutants could not be complemented by auxin, indicating that auxin functions upstream of ethylene. The PHYTOCHROME INTERACTING FACTORS (PIFs) PIF3, PIF4, and PIF5 were previously shown to be important regulators of hypocotyl elongation. We now show that, in contrast to pif4 and pif5 mutants, the reduced hypocotyl length in pif3 cannot be rescued by either ACC or auxin. In line with this, treatment with ethylene or auxin inhibitors reduced hypocotyl elongation in PIF4 overexpressor (PIF4ox) and PIF5ox but not PIF3ox plants. PIF3 promoter activity was strongly reduced under -DIF but could be restored by auxin application in an ACC Synthase-dependent manner. Combined, these results show that PIF3 regulates hypocotyl length downstream, whereas PIF4 and PIF5 regulate hypocotyl length upstream of an auxin and ethylene cascade. We show that, under -DIF, lower auxin biosynthesis activity limits the signaling in this pathway, resulting in low activity of PIF3 and short hypocotyls.

Multiscale imaging of plants: current approaches and challenges

We review a set of recent multiscale imaging techniques, producing high-resolution images of interest for plant sciences. These techniques are promising because they match the multiscale structure of plants. However, the use of such high-resolution images is challenging in the perspective of their application to high-throughput phenotyping on large populations of plants, because of the memory cost for their data storage and the computational cost for their processing to extract information. We discuss how this renews the interest for multiscale image processing tools such as wavelets, fractals and recent variants to analyse such high-resolution images.

TRiP: Tracking Rhythms in Plants, an automated leaf movement analysis program for circadian period estimation

BACKGROUND

A well characterized output of the circadian clock in plants is the daily rhythmic movement of leaves. This process has been used extensively in Arabidopsis to estimate circadian period in natural accessions as well as mutants with known defects in circadian clock function. Current methods for estimating circadian period by leaf movement involve manual steps throughout the analysis and are often limited to analyzing one leaf or cotyledon at a time.

RESULTS

In this study, we describe the development of TRiP (Tracking Rhythms in Plants), a new method for estimating circadian period using a motion estimation algorithm that can be applied to whole plant images. To validate this new method, we apply TRiP to a Recombinant Inbred Line (RIL) population in Arabidopsis using our high-throughput imaging platform. We begin imaging at the cotyledon stage and image through the emergence of true leaves. TRiP successfully tracks the movement of cotyledons and leaves without the need to select individual leaves to be analyzed.

CONCLUSIONS

TRiP is a program for analyzing leaf movement by motion estimation that enables high-throughput analysis of large populations of plants. TRiP is also able to analyze plant species with diverse leaf morphologies. We have used TRiP to estimate period for 150 Arabidopsis RILs as well as 5 diverse plant species, highlighting the broad applicability of this new method.

Measuring synchrony in the mammalian central circadian circuit

Circadian clocks control daily rhythms in physiology and behavior across all phyla. These rhythms are intrinsic to individual cells that must synchronize to their environment and to each other to anticipate daily events. Recent advances in recording from large numbers of cells for many circadian cycles have enabled researchers to begin to evaluate the mechanisms and consequences of intercellular circadian synchrony. Consequently, methods have been adapted to estimate the period, phase, and amplitude of individual circadian cells and calculate synchrony between cells. Stable synchronization requires that the cells share a common period. As a result, synchronized cells maintain constant phase relationships to each (e.g., with cell 1 peaking an hour before cell 2 each cycle). This chapter reviews how circadian rhythms are recorded from single mammalian cells and details methods for measuring their period and phase synchrony. These methods have been useful, for example, in showing that specific neuropeptides are essential to maintain synchrony among circadian cells.

Differentially phased leaf growth and movements in Arabidopsis depend on coordinated circadian and light regulation

In contrast to vastly studied hypocotyl growth, little is known about diel regulation of leaf growth and its coordination with movements such as changes in leaf elevation angle (hyponasty). We developed a 3D live-leaf growth analysis system enabling simultaneous monitoring of growth and movements. Leaf growth is maximal several hours after dawn, requires light, and is regulated by daylength, suggesting coupling between growth and metabolism. We identify both blade and petiole positioning as important components of leaf movements in Arabidopsis thaliana and reveal a temporal delay between growth and movements. In hypocotyls, the combination of circadian expression of PHYTOCHROME INTERACTING FACTOR4 (PIF4) and PIF5 and their light-regulated protein stability drives rhythmic hypocotyl elongation with peak growth at dawn. We find that PIF4 and PIF5 are not essential to sustain rhythmic leaf growth but influence their amplitude. Furthermore, EARLY FLOWERING3, a member of the evening complex (EC), is required to maintain the correct phase between growth and movement. Our study shows that the mechanisms underlying rhythmic hypocotyl and leaf growth differ. Moreover, we reveal the temporal relationship between leaf elongation and movements and demonstrate the importance of the EC for the coordination of these phenotypic traits.

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

Although quantitative characterization of growth phenotypes is of key importance for the understanding of essential networks driving plant growth, the majority of growth-related genes are still being identified based on qualitative visual observations and/or single-endpoint quantitative measurements. We developed an in vitro growth imaging system (IGIS) to perform time-resolved analysis of rosette growth. In this system, Arabidopsis plants are grown in Petri dishes mounted on a rotating disk, and images of each plate are taken on an hourly basis. Automated image analysis was developed in order to obtain several growth-related parameters, such as projected rosette area, rosette relative growth rate, compactness and stockiness, over time. To illustrate the use of the platform and the resulting data, we present the results for the growth response of Col-0 plants subjected to three mild stress conditions. Although the reduction in rosette area was relatively similar at 19 days after stratification, the time-lapse analysis demonstrated that plants react differently to salt, osmotic and oxidative stress. The rosette area was altered at various time points during development, and leaf movement and shape parameters were also affected differently. We also used the IGIS to analyze in detail the growth behavior of mutants with enhanced leaf size. Analysis of several growth-related parameters over time in these mutants revealed several specificities in growth behavior, underlining the high complexity of leaf growth coordination. These results demonstrate that time-resolved imaging of in vitro rosette growth generates a better understanding of growth phenotypes than endpoint measurements.

Antiphase light and temperature cycles affect PHYTOCHROME B-controlled ethylene sensitivity and biosynthesis, limiting leaf movement and growth of Arabidopsis

In the natural environment, days are generally warmer than the night, resulting in a positive day/night temperature difference (+DIF). Plants have adapted to these conditions, and when exposed to antiphase light and temperature cycles (cold photoperiod/warm night [-DIF]), most species exhibit reduced elongation growth. To study the physiological mechanism of how light and temperature cycles affect plant growth, we used infrared imaging to dissect growth dynamics under +DIF and -DIF in the model plant Arabidopsis (Arabidopsis thaliana). We found that -DIF altered leaf growth patterns, decreasing the amplitude and delaying the phase of leaf movement. Ethylene application restored leaf growth in -DIF conditions, and constitutive ethylene signaling mutants maintain robust leaf movement amplitudes under -DIF, indicating that ethylene signaling becomes limiting under these conditions. In response to -DIF, the phase of ethylene emission advanced 2 h, but total ethylene emission was not reduced. However, expression analysis on members of the 1-aminocyclopropane-1-carboxylic acid (ACC) synthase ethylene biosynthesis gene family showed that ACS2 activity is specifically suppressed in the petiole region under -DIF conditions. Indeed, petioles of plants under -DIF had reduced ACC content, and application of ACC to the petiole restored leaf growth patterns. Moreover, acs2 mutants displayed reduced leaf movement under +DIF, similar to wild-type plants under -DIF. In addition, we demonstrate that the photoreceptor PHYTOCHROME B restricts ethylene biosynthesis and constrains the -DIF-induced phase shift in rhythmic growth. Our findings provide a mechanistic insight into how fluctuating temperature cycles regulate plant growth.

Cell to whole-plant phenotyping: the best is yet to come

Imaging and image processing have revolutionized plant phenotyping and are now a major tool for phenotypic trait measurement. Here we review plant phenotyping systems by examining three important characteristics: throughput, dimensionality, and resolution. First, whole-plant phenotyping systems are highlighted together with advances in automation that enable significant throughput increases. Organ and cellular level phenotyping and its tools, often operating at a lower throughput, are then discussed as a means to obtain high-dimensional phenotypic data at elevated spatial and temporal resolution. The significance of recent developments in sensor technologies that give access to plant morphology and physiology-related traits is shown. Overall, attention is focused on spatial and temporal resolution because these are crucial aspects of imaging procedures in plant phenotyping systems.