Corrigendum: Proximal hyperspectral imaging detects diurnal and drought-induced changes in maize physiology.

[This corrects the article DOI: 10.3389/fpls.2021.640914.].

Monitoring of drought stress and transpiration rate using proximal thermal and hyperspectral imaging in an indoor automated plant phenotyping platform.

BACKGROUND: Thermography is a popular tool to assess plant water-use behavior, as plant temperature is influenced by transpiration rate, and is commonly used in field experiments to detect plant water deficit. Its application in indoor automated phenotyping platforms is still limited and mainly focuses on differences in plant temperature between genotypes or treatments, instead of estimating stomatal conductance or transpiration rate. In this study, the transferability of commonly used thermography analysis protocols from the field to greenhouse phenotyping platforms was evaluated. In addition, the added value of combining thermal infrared (TIR) with hyperspectral imaging to monitor drought effects on plant transpiration rate (E) was evaluated. RESULTS: The sensitivity of commonly used TIR indices to detect drought-induced and genotypic differences in water status was investigated in eight maize inbred lines in the automated phenotyping platform PHENOVISION. Indices that normalized plant temperature for vapor pressure deficit and/or air temperature at the time of imaging were most sensitive to drought and could detect genotypic differences in the plants' water-use behavior. However, these indices were not strongly correlated to stomatal conductance and E. The canopy temperature depression index, the crop water stress index and the simplified stomatal conductance index were more suitable to monitor these traits, and were consequently used to develop empirical E prediction models by combining them with hyperspectral indices and/or environmental variables. Different modeling strategies were evaluated, including single index-based, machine learning and mechanistic models. Model comparison showed that combining multiple TIR indices in a random forest model can improve E prediction accuracy, and that the contribution of the hyperspectral data is limited when multiple indices are used. However, the empirical models trained on one genotype were not transferable to all eight inbred lines. CONCLUSION: Overall, this study demonstrates that existing TIR indices can be used to monitor drought stress and develop E prediction models in an indoor setup, as long as the indices normalize plant temperature for ambient air temperature or relative humidity.

Zinc loading in urea-formaldehyde nanocomposites increases nitrogen and zinc micronutrient fertilization efficiencies in poor sand substrate.

Agricultural output needs significant increases to feed the growing population. Fertilizers are essential for plant production systems, with nitrogen (N) being the most limiting nutrient for plant growth. It is commonly supplied to crops as urea. Still, due to volatilization, up to 50 % of the total N application is lost. Slow or controlled release fertilizers are being developed to reduce these losses. The co-application of zinc (Zn) as a micronutrient can increase N absorption. Thus, we hypothesize that the controlled delivery of both nutrients (N and Zn) in an integrated system can improve uptake efficiency. Here we demonstrate an optimized fertilizer nanocomposite based on urea:urea-formaldehyde matrix loaded with ZnSO4 or ZnO. This nanocomposite effectively stimulates maize development, with consequent adequate N uptake, in an extreme condition - a very nutrient-poor sand substrate. Our results indicate that the Zn co-application is beneficial for plant development. However, there were advantages for ZnO due to its high Zn content. We discuss that the dispersion favors the Zn delivery as the nanoparticulated oxide in the matrix. Concerning maize development, we found that root morphology is altered in the presence of the fertilizer nanocomposite. Increased root length and surface area may improve soil nutrient uptake, potentially accompanied by increased root exudation of essential compounds for N release from the composite structure.

Morphological and Physiological Traits Associated with Yield under Reduced Irrigation in Chilean Coastal Lowland Quinoa.

Quinoa (Chenopodium quinoa Willd.) is a genetically diverse crop that has gained popularity in recent years due to its high nutritional content and ability to tolerate abiotic stresses such as salinity and drought. Varieties from the coastal lowland ecotype are of particular interest due to their insensitivity to photoperiod and their potential to be cultivated in higher latitudes. We performed a field experiment in the southern Atacama Desert in Chile to investigate the responses to reduced irrigation of nine previously selected coastal lowland self-pollinated (CLS) lines and the commercial cultivar Regalona. We found that several lines exhibited a yield and seed size superior to Regalona, also under reduced irrigation. Plant productivity data were analyzed together with morphological and physiological traits measured at the visible inflorescence stage to estimate the contribution of these traits to differences between the CLS lines and Regalona under full and reduced irrigation. We applied proximal sensing methods and found that thermal imaging provided a promising means to estimate variation in plant water use relating to yield, whereas hyperspectral imaging separated lines in a different way, potentially related to photosynthesis as well as water use.

Drought affects the rate and duration of organ growth but not inter-organ growth coordination.

Drought at flowering and grain filling greatly reduces maize (Zea mays) yield. Climate change is causing earlier and longer-lasting periods of drought, which affect the growth of multiple maize organs throughout development. To study how long periods of water deficit impact the dynamic nature of growth, and to determine how these relate to reproductive drought, we employed a high-throughput phenotyping platform featuring precise irrigation, imaging systems, and image-based biomass estimations. Prolonged drought resulted in a reduction of growth rate of individual organs-though an extension of growth duration partially compensated for this-culminating in lower biomass and delayed flowering. However, long periods of drought did not affect the highly organized succession of maximal growth rates of the distinct organs, i.e. leaves, stems, and ears. Two drought treatments negatively affected distinct seed yield components: Prolonged drought mainly reduced the number of spikelets, and drought during the reproductive period increased the anthesis-silking interval. The identification of these divergent biomass and yield components, which were affected by the shift in duration and intensity of drought, will facilitate trait-specific breeding toward future climate-resilient crops.

Proximal Hyperspectral Imaging Detects Diurnal and Drought-Induced Changes in Maize Physiology.

Hyperspectral imaging is a promising tool for non-destructive phenotyping of plant physiological traits, which has been transferred from remote to proximal sensing applications, and from manual laboratory setups to automated plant phenotyping platforms. Due to the higher resolution in proximal sensing, illumination variation and plant geometry result in increased non-biological variation in plant spectra that may mask subtle biological differences. Here, a better understanding of spectral measurements for proximal sensing and their application to study drought, developmental and diurnal responses was acquired in a drought case study of maize grown in a greenhouse phenotyping platform with a hyperspectral imaging setup. The use of brightness classification to reduce the illumination-induced non-biological variation is demonstrated, and allowed the detection of diurnal, developmental and early drought-induced changes in maize reflectance and physiology. Diurnal changes in transpiration rate and vapor pressure deficit were significantly correlated with red and red-edge reflectance. Drought-induced changes in effective quantum yield and water potential were accurately predicted using partial least squares regression and the newly developed Water Potential Index 2, respectively. The prediction accuracy of hyperspectral indices and partial least squares regression were similar, as long as a strong relationship between the physiological trait and reflectance was present. This demonstrates that current hyperspectral processing approaches can be used in automated plant phenotyping platforms to monitor physiological traits with a high temporal resolution.

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.

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

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

High resolution imaging of maize (Zea mays) leaf temperature in the field: the key role of the regions of interest.

The use of remote sensors (thermometers and cameras) to analyse crop water status in field conditions is fraught with several difficulties. In particular, average canopy temperature measurements are affected by the mixture of soil and green regions, the mutual shading of leaves and the variability of absorbed radiation. The aim of the study was to analyse how the selection of different 'regions of interest' (ROI) in canopy images affect the variability of the resulting temperature averages. Using automated image segmentation techniques we computed the average temperature in four nested ROI of decreasing size, from the whole image down to the sunlit fraction of a leaf located in the upper part of the canopy. The study was conducted on maize (Zea mays L.) at the flowering stage, for its large leaves and well structured canopy. Our results suggest that, under these conditions, the ROI comprising the sunlit fraction of a leaf located in the upper part of the canopy should be analogous to the single leaf approach (in controlled conditions) that allows the estimation of stomatal conductance or plant water potential.

Phenotyping the kinematics of leaf development in flowering plants: recommendations and pitfalls.

Leaves of flowering plants are produced from the shoot apical meristem at regular intervals and they grow according to a developmental program that is determined by both genetic and environmental factors. Detailed frameworks for multiscale dynamic analyses of leaf growth have been developed in order to identify and interpret phenotypic differences caused by either genetic or environmental variations. They revealed that leaf growth dynamics are non-linearly and nonhomogeneously distributed over the lamina, in the leaf tissues and cells. The analysis of the variability in leaf growth, and its underlying processes, has recently gained momentum with the development of automated phenotyping platforms that use various technologies to record growth at different scales and at high throughput. These modern tools are likely to accelerate the characterization of gene function and the processes that underlie the control of shoot development. Combined with powerful statistical analyses, trends have emerged that may have been overlooked in low throughput analyses. However, in many examples, the increase in throughput allowed by automated platforms has led to a decrease in the spatial and/or temporal resolution of growth analyses. Concrete examples presented here indicate that simplification of the dynamic leaf system, without consideration of its spatial and temporal context, can lead to important misinterpretations of the growth phenotype.

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.

Systems-based analysis of Arabidopsis leaf growth reveals adaptation to water deficit.

Leaves have a central role in plant energy capture and carbon conversion and therefore must continuously adapt their development to prevailing environmental conditions. To reveal the dynamic systems behaviour of leaf development, we profiled Arabidopsis leaf number six in depth at four different growth stages, at both the end-of-day and end-of-night, in plants growing in two controlled experimental conditions: short-day conditions with optimal soil water content and constant reduced soil water conditions. We found that the lower soil water potential led to reduced, but prolonged, growth and an adaptation at the molecular level without a drought stress response. Clustering of the protein and transcript data using a decision tree revealed different patterns in abundance changes across the growth stages and between end-of-day and end-of-night that are linked to specific biological functions. Correlations between protein and transcript levels depend on the time-of-day and also on protein localisation and function. Surprisingly, only very few of >1700 quantified proteins showed diurnal abundance fluctuations, despite strong fluctuations at the transcript level.

Structural assessment of the impact of environmental constraints on Arabidopsis thaliana leaf growth: a 3D approach.

Light and soil water content affect leaf surface area expansion through modifications in epidermal cell numbers and area, while effects on leaf thickness and mesophyll cell volumes are far less documented. Here, three-dimensional imaging was applied in a study of Arabidopsis thaliana leaf growth to determine leaf thickness and the cellular organization of mesophyll tissues under moderate soil water deficit and two cumulative light conditions. In contrast to surface area, thickness was highly conserved in response to water deficit under both low and high cumulative light regimes. Unlike epidermal and palisade mesophyll tissues, no reductions in cell number were observed in the spongy mesophyll; cells had rather changed in volume and shape. Furthermore, leaf features of a selection of genotypes affected in leaf functioning were analysed. The low-starch mutant pgm had very thick leaves because of unusually large palisade mesophyll cells, together with high levels of photosynthesis and stomatal conductance. By means of an open stomata mutant and a 9-cis-epoxycarotenoid dioxygenase overexpressor, it was shown that stomatal conductance does not necessarily have a major impact on leaf dimensions and cellular organization, pointing to additional mechanisms for the control of CO(2) diffusion under high and low stomatal conductance, respectively.

Automated motion estimation of root responses to sucrose in two Arabidopsis thaliana genotypes using confocal microscopy.

Root growth is a highly dynamic process influenced by genetic background and environment. This paper reports the development of R scripts that enable root growth kinematic analysis that complements a new motion analysis tool: PlantVis. Root growth of Arabidopsis thaliana expressing a plasma membrane targeted GFP (C24 and Columbia 35S:LTI6b-EGFP) was imaged using time-lapse confocal laser scanning microscopy. Displacement of individual pixels in the time-lapse sequences was estimated automatically by PlantVis, producing dense motion vector fields. R scripts were developed to extract kinematic growth parameters and report displacement to ± 0.1 pixel. In contrast to other currently available tools, Plantvis-R delivered root velocity profiles without interpolation or averaging across the root surface and also estimated the uncertainty associated with tracking each pixel. The PlantVis-R analysis tool has a range of potential applications in root physiology and gene expression studies, including linking motion to specific cell boundaries and analysis of curvature. The potential for quantifying genotype × environment interactions was examined by applying PlantVis-R in a kinematic analysis of root growth of C24 and Columbia, under contrasting carbon supply. Large genotype-dependent effects of sucrose were recorded. C24 exhibited negligible differences in elongation zone length and elongation rate but doubled the density of lateral roots in the presence of sucrose. Columbia, in contrast, increased its elongation zone length and doubled its elongation rate and the density of lateral roots.

PHENOPSIS DB: an information system for Arabidopsis thaliana phenotypic data in an environmental context.

BACKGROUND: Renewed interest in plant×environment interactions has risen in the post-genomic era. In this context, high-throughput phenotyping platforms have been developed to create reproducible environmental scenarios in which the phenotypic responses of multiple genotypes can be analysed in a reproducible way. These platforms benefit hugely from the development of suitable databases for storage, sharing and analysis of the large amount of data collected. In the model plant Arabidopsis thaliana, most databases available to the scientific community contain data related to genetic and molecular biology and are characterised by an inadequacy in the description of plant developmental stages and experimental metadata such as environmental conditions. Our goal was to develop a comprehensive information system for sharing of the data collected in PHENOPSIS, an automated platform for Arabidopsis thaliana phenotyping, with the scientific community. DESCRIPTION: PHENOPSIS DB is a publicly available (URL: http://bioweb.supagro.inra.fr/phenopsis/) information system developed for storage, browsing and sharing of online data generated by the PHENOPSIS platform and offline data collected by experimenters and experimental metadata. It provides modules coupled to a Web interface for (i) the visualisation of environmental data of an experiment, (ii) the visualisation and statistical analysis of phenotypic data, and (iii) the analysis of Arabidopsis thaliana plant images. CONCLUSIONS: Firstly, data stored in the PHENOPSIS DB are of interest to the Arabidopsis thaliana community, particularly in allowing phenotypic meta-analyses directly linked to environmental conditions on which publications are still scarce. Secondly, data or image analysis modules can be downloaded from the Web interface for direct usage or as the basis for modifications according to new requirements. Finally, the structure of PHENOPSIS DB provides a useful template for the development of other similar databases related to genotype×environment interactions.

High-contrast three-dimensional imaging of the Arabidopsis leaf enables the analysis of cell dimensions in the epidermis and mesophyll.

BACKGROUND: Despite the wide spread application of confocal and multiphoton laser scanning microscopy in plant biology, leaf phenotype assessment still relies on two-dimensional imaging with a limited appreciation of the cells' structural context and an inherent inaccuracy of cell measurements. Here, a successful procedure for the three-dimensional imaging and analysis of plant leaves is presented. RESULTS: The procedure was developed based on a range of developmental stages, from leaf initiation to senescence, of soil-grown Arabidopsis thaliana (L.) Heynh. Rigorous clearing of tissues, made possible by enhanced leaf permeability to clearing agents, allowed the optical sectioning of the entire leaf thickness by both confocal and multiphoton microscopy. The superior image quality, in resolution and contrast, obtained by the latter technique enabled the three-dimensional visualisation of leaf morphology at the individual cell level, cell segmentation and the construction of structural models. Image analysis macros were developed to measure leaf thickness and tissue proportions, as well as to determine for the epidermis and all layers of mesophyll tissue, cell density, volume, length and width. For mesophyll tissue, the proportion of intercellular spaces and the surface areas of cells were also estimated. The performance of the procedure was demonstrated for the expanding 6th leaf of the Arabidopsis rosette. Furthermore, it was proven to be effective for leaves of another dicotyledon, apple (Malus domestica Borkh.), which has a very different cellular organisation. CONCLUSIONS: The pipeline for the three-dimensional imaging and analysis of plant leaves provides the means to include variables on internal tissues in leaf growth studies and the assessment of leaf phenotypes. It also allows the visualisation and quantification of alterations in leaf structure alongside changes in leaf functioning observed under environmental constraints. Data obtained using this procedure can further be integrated in leaf development and functioning models.

Extraction and partial characterization of polyphenol oxidase from banana (Musa acuminata Grande naine) roots.

Polyphenol oxidase activity (PPO, EC 1.14.18.1, monophenol monooxygenase, and EC 1.10.3.2, o-diphenoloxidase) has been extensively studied in banana fruit for its role in enzymatic browning. Rapid discolouration of leaf, stem and root tissue after injury and strong pigmentation of tissue extracts indicate that PPO and phenolic compounds are ubiquitous in vegetative tissue of banana as well. They hamper biochemical and molecular studies in banana, as cumbersome adaptations of extraction protocols are required. On the other hand, PPO and phenolic compounds could be an important part of the plant's defence system against pests and diseases, including root parasitic nematodes. To facilitate future studies in this area, extraction and assay conditions for PPO from roots of banana (Musa acuminata AAA, Grande naine) were optimized. Highest enzyme activities were obtained in a 0.2 M phosphate buffer at pH 7.0 with 5% insoluble polyvinylpyrrolidone and 0.25% Triton X-100. The lowest K(m) values were obtained for dopamine and D-catechin. Monophenolase activity was shown with p-cresol. Banana root PPO was strongly inhibited by dithiothreitol and sodium metabisulfite. In root sections, oxidation of dopamine strongly co-localized with aerenchyma in the cortex. The experiments revealed indications for the involvement of root PPO and dopamine in resistance of banana against the parasitic nematode Radopholus similis.