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International audience; Wood is a complex tissue that fulfills three major functions in trees: water conduction, mechanical support and nutrient storage. In Angiosperm trees, vessels, fibers and parenchyma rays are respectively assigned to these functions. Cell wall composition and structure strongly varies according to cell type, developmental stages and environmental conditions. This complexity can therefore hinder the study of the molecular mechanisms of wood formation, underlying the construction of its properties. However, this can be circumvented thanks to the development of cell-specific approaches and microphenotyping. Here, we present a non-destructive microphenotyping method based on attenuated total reflectance-Fourier transformed infrared (ATR-FTIR) microspectroscopy. We applied this technique to three types of poplar wood: normal wood of staked trees (NW), tension and opposite wood of artificially tilted trees (TW, OW). TW is produced by angiosperm trees in response to mechanical strains and is characterized by the presence of G fibers, exhibiting a thick gelatinous extra-layer, named G-layer, located in place of the usual S2 and/or S3 layers. By contrast, OW located on the opposite side of the trunk is totally deprived of fibers with G-layers. We developed a workflow for hyperspectral image analysis with both automatic pixel clustering according to cell wall types and identification of differentially absorbed wavenumbers (DAWNs). As pixel clustering failed to assign pixels to ray S-layers with sufficient efficiency, the IR profiling and identification of DAWNs were restricted to fiber and vessel cell walls. As reported elsewhere, this workflow identified cellulose as the main component of the G-layers, while the amount in acetylated xylans and lignins were shown to be reduced. These results validate ATR-FTIR technique for in situ characterization of G layers. In addition, this study brought new information about IR profiling of S-layers in TW, OW and NW. While OW and NW exhibited similar profiles, TW fibers S-layers combined characteristics of TW G-layers and of regular fiber S-layers. Unexpectedly, vessel S-layers of the three kinds of wood showed significant differences in IR profiling. In conclusion, ATR-FTIR microspectroscopy offers new possibilities for studying cell wall composition at the cell level.

International audience; Plant phenotyping consists in the observation of physical and biochemical traits of plant genotypes in response to environmental conditions. Challenges , in particular in context of climate change and food security, are numerous. High-throughput platforms have been introduced to observe the dynamic growth of a large number of plants in different environmental conditions. Instead of considering a few genotypes at a time (as it is the case when phenomic traits are measured manually), such platforms make it possible to use completely new kinds of approaches. However, the data sets produced by such widely instrumented platforms are huge, constantly augmenting and produced by increasingly complex experiments, reaching a point where distributed computation is mandatory to extract knowledge from data. In this paper, we introduce InfraPhenoGrid, the infrastructure we designed and deploy to efficiently manage data sets produced by the PhenoArch plant phenomics platform in the context of the French Phenome Project. Our solution consists in deploying scientific workflows on a Grid using a middle-ware to pilot workflow executions. Our approach is user-friendly in the sense that despite the intrinsic complexity of the infrastructure, running scientific workflows and understanding results obtained (using provenance information) is kept as simple as possible for end-users.

UMR AGAP - équipe AFEF - Architecture et fonctionnement des espèces fruitières; Numerous agronomical applications of remote sensing have been proposed in recent years, including water stress assessment at field by thermal imagery. The miniaturization of thermal cameras allows carrying them onboard the unmanned aerial vehicles (UAVs), but these systems have no temperature control and, consequently, drifts during data acquisition have to be carefully corrected. This manuscript presents a comprehensive methodology for radiometric correction of UAV remotely-sensed thermal images to obtain (combined with visible and near-infrared data) multispectral ortho-mosaics, as a previous step for further image-based assessment of tree response to water stress. On summer 2013, UAV flights were performed over an apple tree orchard located in Southern France, and 4 dates and 5 h of the day were tested. The 6400 m(2) field plot comprised 520 apple trees, half well-irrigated and half submitted to progressive summer water stress. Temperatures of four different on-ground stable reference targets were continuously measured by thermo-radiometers for radiometric calibration purposes. By using self-developed software, frames were automatically extracted from the thermal video files, and then radiometrically calibrated using the thermal targets data. Once ortho-mosaics were obtained, root mean squared error (RMSE) was calculated. The accuracy obtained allowed multi-temporal mosaic comparison. Results showed a good relationship between calibrated images and on-ground data. Significantly higher canopy temperatures were found in water-stressed trees compared to well-irrigated ones. As high resolution field ortho-mosaics were obtained, comparison between trees opens the possibility of using multispectral data as phenotypic variables for the characterization of individual plant response to drought.

GnpIS is a data repository for plant phenomics that stores whole field and greenhouse experimental data including environment measures. It allows long-term access to datasets following the FAIR principles: Findable, Accessible, Interoperable, and Reusable, by using a flexible and original approach. It is based on a generic and ontology driven data model and an innovative software architecture that uncouples data integration, storage, and querying. It takes advantage of international standards including the Crop Ontology, MIAPPE, and the Breeding API. GnpIS allows handling data for a wide range of species and experiment types, including multiannual perennial plants experimental network or annual plant trials with either raw data, i.e., direct measures, or computed traits. It also ensures the integration and the interoperability among phenotyping datasets and with genotyping data. This is achieved through a careful curation and annotation of the key resources conducted in close collaboration with the communities providing data. Our repository follows the Open Science data publication principles by ensuring citability of each dataset. Finally, GnpIS compliance with international standards enables its interoperability with other data repositories hence allowing data links between phenotype and other data types. GnpIS can therefore contribute to emerging international federations of information systems.

International audience; The dynamics of the Green Leaf Area Index (GLAI) is of great interest for numerous applications such as yield prediction and plant breeding. We present a high-throughput model-assisted method for characterizing GLAI dynamics in maize (Zea mays subsp. mays) using multispectral imagery acquired from an Unmanned Aerial Vehicle (UAV). Two trials were conducted with a high diversity panel of 400 lines under well-watered and water-deficient treatments in 2016 and 2017. For each UAV flight, we first derived GLAI estimates from empirical relationships between the multispectral reflectance and ground level measurements of GLAI achieved over a small sample of microplots. We then fitted a simple but physiologically sound GLAI dynamics model over the GLAI values estimated previously. Results show that GLAI dynamics was estimated accurately throughout the cycle (R2 > 0.9). Two parameters of the model, biggest leaf area and leaf longevity, were also estimated successfully. We showed that GLAI dynamics and the parameters of the fitted model are highly heritable (0.65 ≤ H2 ≤ 0.98), responsive to environmental conditions, and linked to yield and drought tolerance. This method, combining growth modeling, UAV imagery and simple non-destructive field measurements, provides new high-throughput tools for understanding the adaptation of GLAI dynamics and its interaction with the environment. GLAI dynamics is also a promising trait for crop breeding, and paves the way for future genetic studies.

Breeding for crops in the context of climate change necessitates phenotyping tools for roots in field conditions. Such in-field phenotyping requires the development of rapid and non-destructive measurement techniques for the screening of relevant root traits under sub-optimal conditions. In this study, we used scanner-based minirhizotrons to measure in situ the root length and surface/volume densities of roots for four wheat varieties, under four different growth conditions: irrigated and rainfed coupled with optimal and sub-optimal N fertilization under a Mediterranean climate. For all the treatments, grain yield correlates with minirhizotron-based root surface density measured at anthesis (r2 = 0.48). Irrigated and rainfed conditions led to contrasted relations between roots and grain yield: no correlation was found in irrigated plots, while under rainfed conditions and sub-optimal fertilization, the higher yields are related to a higher root colonization of the deeper soil layers (r2 = 0.40). Shoot biomass was correlated to grain yield in irrigated conditions, but not in rainfed conditions. However, for the latter, the total root weight, the proportion of which being mainly located in the top soil, is not related to the grain yield. In this way, we show the relationship between these higher grain yields and a stress avoidance mechanism of the root system characterized by a higher root density in the deep soil layers. Thus, unlike shoot biomass measurements, scanner-based minirhizotron allows the direct detection of such a stress-related root development, and therefore opens the door to a better prediction of grain yield.

International audience; Progress in remote sensing and robotic technologies decreases the hardware costs of phenotyping. Here, we first review cost-effective imaging devices and environmental sensors, and present a trade-off between investment and manpower costs. We then discuss the structure of costs in various real-world scenarios. Hand-held low-cost sensors are suitable for quick and infrequent plant diagnostic measurements. In experiments for genetic or agronomic analyses, (i) major costs arise from plant handling and manpower; (ii) the total costs per pot/microplot are similar in robotized platform or field experiments with drones, hand-held or robotized ground vehicles; (iii) the cost of vehicles carrying sensors represents only 5-26% of the total costs. These conclusions depend on the context, in particular for labor cost, the quantitative demand of phenotyping and the number of days available for phenotypic measurements due to climatic constraints. Data analysis represents 10-20% of total cost if pipelines have already been developed. A trade-off exists between the initial high cost of pipeline development and labor cost of manual operations. Overall, depending on the context and objectives, “cost-effective” phenotyping may involve either low investment (“affordable phenotyping”), or initial high investments in sensors, vehicles and pipelines that result in higher quality and lower operational costs.

New types of phenotyping tools generate large amounts of data on many aspects of plant physiology and morphology with high spatial and temporal resolution. These new phenotyping data are potentially useful to improve understanding and prediction of complex traits, like yield, that are characterized by strong environmental context dependencies, i.e., genotype by environment interactions. For an evaluation of the utility of new phenotyping information, we will look at how this information can be incorporated in different classes of genotype-to-phenotype (G2P) models. G2P models predict phenotypic traits as functions of genotypic and environmental inputs. In the last decade, access to high-density single nucleotide polymorphism markers (SNPs) and sequence information has boosted the development of a class of G2P models called genomic prediction models that predict phenotypes from genome wide marker profiles. The challenge now is to build G2P models that incorporate simultaneously extensive genomic information alongside with new phenotypic information. Beyond the modification of existing G2P models, new G2P paradigms are required. We present candidate G2P models for the integration of genomic and new phenotyping information and illustrate their use in examples. Special attention will be given to the modelling of genotype by environment interactions. The G2P models provide a framework for model based phenotyping and the evaluation of the utility of phenotyping information in the context of breeding programs. ispartof: Plant Science vol:282 pages:23-39 ispartof: location:MEXICO, Int Maize & Wheat Improvement Ctr status: Published online

The frequencies of free oscillations of plants, or plant parts, depend on their geometries, stiffnesses, and masses. Besides direct biomechanical interest, free frequencies also provide insights into plant properties that can usually only be measured destructively or with low-throughput techniques (e.g., change in mass, tissue density, or stiffness over development or with stresses). We propose here a new high-throughput method based on the noncontact measurements of the free frequencies of the standing plant. The plant is excited by short air pulses (typically 100 ms). The resulting motion is recorded by a high speed video camera (100 fps) and processed using fast space and time correlation algorithms. In less than a minute the mechanical behavior of the plant is tested over several directions. The performance and versatility of this method has been tested in three contrasted species: tobacco (Nicotiana benthamian), wheat (Triticum aestivum L.), and poplar (Populus sp.), for a total of more than 4000 data points. In tobacco we show that water stress decreased the free frequency by 15%. In wheat we could detect variations of less than 1 g in the mass of spikes. In poplar we could measure frequencies of both the whole stem and leaves. The work provides insight into new potential directions for development of phenotyping.

Agricultural systems models are complex and tend to be over-parameterized with respect to observational datasets. Practical identifiability analysis based on local sensitivity analysis has proved effective in investigating identifiable parameter sets in environmental models, but has not been applied to agricultural systems models. Here, we demon-strate that identifiability analysis improves experimental design to ensure independent parameter estimation for yield and quality outputs of a complex grassland model. The Pasture Simulation model (PaSim) was used to demonstrate the effectiveness of practical identifiability analysis in designing experiments and measurement protocols within phe-notyping experiments with perennial ryegrass. Virtual experiments were designed combining three factors: frequency of measurements, duration of the experiment. and location of trials. Our results demonstrate that (i) PaSim provides sufficient detail in terms of simulating biomass yield and quality of perennial ryegrass for use in breeding, (ii) typical breeding trials are insufficient to parameterize all influential parameters, (iii) the frequency of measurements is more important than the number of growing seasons to improve the identifiability of PaSim parameters, and (iv) identifiabil-ity analysis provides a sound approach for optimizing the design of multi-location trials. Practical identifiability ana-lysis can play an important role in ensuring proper exploitation of phenotypic data and cost-effective multi-location experimental designs. Considering the growing importance of simulation models, this study supports the design of experiments and measurement protocols in the phenotyping networks that have recently been organized.

We aim at extending methodologies based on T-Lidar scans for providing a description of individual apple tree topology and architecture. Experiments were performed on 2- and 3-year-old trees belonging to a core collection of French apple cultivars. In 2016, ten trees were precisely scanned in winter to assess our ability to extract topological traits. In 2017, the whole core-collection was scanned to estimate tree leaf areas at the end of the summer period. Topological reconstructions on 2016 data were performed with Plantscan3D software using existing algorithms whereas an allometric relationship depending on tree alpha hull volumes was used for estimating tree leaf areas on 2017 data. Topological traits extracted from T-LiDAR data were compared to digitizing data of the same trees and the estimated total leaf areas were compared to manual measurements on a sample of twenty trees displaying large variability in vegetative development. In 2016, topological reconstructions gave promising results with R² values higher than 0.90 for the total number of axes and growth units pet tree and for their mean length when T-Lidar were compared to digitizing data. However, a significant number of short axes or growth units were not detected by the T-Lidar. Comparisons between T-LiDAR and digitizing data showed g ood adequacy if axes were classified depending on their branching order suggesting that our method was relevant for evaluating the number of branching points in the structure. Regarding data collected on leafy trees in 2017, the total leaf area estimated from T-LiDAR was also highly correlated (R²= 0.83) with manual measurements. Forthcoming works are undergoing for improving topological reconstruction algorithms in order to extract topological traits and new variables related to vegetative development on the whole population. Nevertheless this first study suggests that this method could be adapted for phenotypic architectural traits on large tree population on which genetic analyses could be performed.

Plants adapt to the prevailing photoperiod by adjusting growth and flowering to the availability of energy. To understand the molecular changes involved in adaptation to a long-day condition we comprehensively profiled leaf six at the end of the day and the end of the night at four developmental stages on Arabidopsis thaliana plants grown in a 16h photoperiod, and compared the profiles to those from leaf 6 of plants grown in a 8h photoperiod. When Arabidopsis is grown in a long-day photoperiod individual leaf growth is accelerated but whole plant leaf area is decreased because total number of rosette leaves is restricted by the rapid transition to flowering. Carbohydrate measurements in long- and short-day photoperiods revealed that a long photoperiod decreases the extent of diurnal turnover of carbon reserves at all leaf stages. At the transcript level we found that the long-day condition has significantly reduced diurnal transcript level changes than in short-day condition, and that some transcripts shift their diurnal expression pattern. Functional categorisation of the transcripts with significantly different levels in short and long day conditions revealed photoperiod-dependent differences in RNA processing and light and hormone signalling, increased abundance of transcripts for biotic stress response and flavonoid metabolism in long photoperiods, and for photosynthesis and sugar transport in short photoperiods. Furthermore, we found transcript level changes consistent with an early release of flowering repression in the long-day condition. Differences in protein levels between long and short photoperiods mainly reflect an adjustment to the faster growth in long photoperiods. In summary, the observed differences in the molecular profiles of leaf six grown in long- and short-day photoperiods reveal changes in the regulation of metabolism that allow plants to adjust their metabolism to the available light. The data also suggest that energy management is in the two photoperiods fundamentally different as a consequence of photoperiod-dependent energy constraints.

Natural and urban forests worldwide are increasingly threatened by global change resulting from human-mediated factors, including invasions by lethal exotic pathogens. Ash dieback (ADB), incited by the alien invasive fungus Hymenoscyphus fraxineus, has caused large-scale population decline of European ash (Fraxinus excelsior) across Europe, and is threatening to functionally extirpate this tree species. Genetically controlled host resistance is a key element to ensure European ash survival and to restore this keystone species where it has been decimated. We know that a low proportion of the natural population of European ash expresses heritable, quantitative resistance that is stable across environments. To exploit this resource for breeding and restoration efforts, tools that allow for effective and efficient, rapid identification and deployment of superior genotypes are now sorely needed. Here we show that Fourier-transform infrared (FT-IR) spectroscopy of phenolic extracts from uninfected bark tissue, coupled with a model based on soft independent modelling of class analogy (SIMCA), can robustly discriminate between ADB-resistant and susceptible European ash. The model was validated with populations of European ash grown across six European countries. Our work demonstrates that this approach can efficiently advance the effort to save such fundamental forest resource in Europe and elsewhere.

Background In order to efficiently determine genotypic differences in rooting patterns of crops, novel hardware and software are needed simultaneously to characterize dynamics of root development. Results We describe a prototype robotic monitoring platform—the RhizoChamber-Monitor for analyzing growth patterns of plant roots automatically. The RhizoChamber-Monitor comprises an automatic imaging system for acquiring sequential images of roots which grow on a cloth substrate in custom rhizoboxes, an automatic irrigation system and a flexible shading arrangement. A customized image processing software was developed to analyze the spatio-temporal dynamics of root growth from time-course images of multiple plants. This software can quantify overall growth of roots and extract detailed growth traits (e.g. dynamics of length and diameter) of primary roots and of individual lateral roots automatically. It can also identify local growth traits of lateral roots (pseudo-mean-length and pseudo-maximum-length) semi-automatically. Two cotton genotypes were used to test both the physical platform and the analysis software. Conclusions The combination of hardware and software is expected to facilitate quantification of root geometry and its spatio-temporal growth patterns, and therefore to provide opportunities for high-throughput root phenotyping in support of crop breeding to optimize root architecture.

The capacity of LiDAR and Unmanned Aerial Vehicles (UAVs) to provide plant height estimates as a high-throughput plant phenotyping trait was explored. An experiment over wheat genotypes conducted under well watered and water stress modalities was conducted. Frequent LiDAR measurements were performed along the growth cycle using a phénomobile unmanned ground vehicle. UAV equipped with a high resolution RGB camera was flying the experiment several times to retrieve the digital surface model from structure from motion techniques. Both techniques provide a 3D dense point cloud from which the plant height can be estimated. Plant height first defined as the z-value for which 99.5% of the points of the dense cloud are below. This provides good consistency with manual measurements of plant height (RMSE = 3.5 cm) while minimizing the variability along each microplot. Results show that LiDAR and structure from motion plant height values are always consistent. However, a slight under-estimation is observed for structure from motion techniques, in relation with the coarser spatial resolution of UAV imagery and the limited penetration capacity of structure from motion as compared to LiDAR. Very high heritability values (H2> 0.90) were found for both techniques when lodging was not present. The dynamics of plant height shows that it carries pertinent information regarding the period and magnitude of the plant stress. Further, the date when the maximum plant height is reached was found to be very heritable (H2> 0.88) and a good proxy of the flowering stage. Finally, the capacity of plant height as a proxy for total above ground biomass and yield is discussed.

Acclimation to water deficit (WD) enables plants to maintain growth under unfavorable environmental conditions, although the mechanisms are not completely understood. In this study, the natural variation of long-term acclimation to moderate and severe soil WD was investigated in 18 Arabidopsis (Arabidopsis thaliana) accessions using PHENOPSIS, an automated phenotyping platform. Soil water content was adjusted at an early stage of plant development and maintained at a constant level until reproductive age was achieved. The accessions were selected based on the expression levels of ANNEXIN1, a drought-related marker. Severe WD conditions had a greater effect on most of the measured morphophysiological traits than moderate WD conditions. Multivariate analyses indicated that trait responses associated with plant size and water management drove most of the variation. Accessions with similar responses at these two levels were grouped in clusters that displayed different response strategies to WD. The expression levels of selected stress-response genes revealed large natural variation under WD conditions. Responses of morphophysiological traits, such as projected rosette area, transpiration rate, and rosette water content, were correlated with changes in the expression of stress-related genes, such as NINE-CIS-EPOXYCAROTENOID DIOXYGENASE3 and N-MYC DOWNREGULATED-LIKE1 (NDL1), in response to WD. Interestingly, the morphophysiological acclimation response to WD also was reflected in the gene expression levels (most notably those of NDL1, CHALCONE SYNTHASE, and MYB DOMAIN PROTEIN44) in plants cultivated under well-watered conditions. Our results may lead to the development of biomarkers and predictors of plant morphophysiological responses based on gene expression patterns.

Developmental responses to temperature are critical to yield formation in crops and perennial grassland species. However, their characterisation over a broad range of temperatures relevant to climate change studies has been limited in these species. The present study sought to determine the non-linear developmental responses to temperature of two major grassland species, tall fescue (Festuca arundinacea) and alfalfa (Medicago sativa), and to assess i) whether a coordinated response occurred between different developmental processes, ii) if genotypes from contrasting origins differed in their responses, and iii) to quantify how a lack of coordination and genetic variability might affect estimates of thermal time progression under present and future climate scenarios. Two series of experiments were carried out under controlled conditions with eight constant temperatures ranging from 5 degrees C to 40 degrees C applied to seedlings and mature plants. Different growth and developmental processes were characterized, including shoot development and radicle, leaf and stem growth. Once normalized to a temperature of 20 degrees C, the temperature responses of the different processes displayed no significant variability between temperate and Mediterranean genotypes. On the other hand, not all developmental processes within a genotype displayed a coordinated response to temperature. Significant departures from the response of shoot development in mature plants were observed regarding several processes, particularly at low and supra-optimal temperatures (up to +/- 5 degrees C and +/- 2 degrees C for minimal and maximal temperature estimates, respectively). Differences were particularly marked relative to node and stem elongation responses. Overall, when using the temperature dependencies of different processes to estimate cumulative thermal time, significant bias was observed in alfalfa when considering stem elongation by comparison with other processes. The consequences for improvements to forage crop models, and for the meta-analyses often used to calibrate them, are discussed.

In recent years, the interest in new technologies for wheat improvement has increased greatly. To screen genetically modified germplasm in conditions more realistic for a field situation we developed a phenotyping platform where transgenic wheat and barley are grown in competition. In this study, we used the platform to (1) test selected promoter and gene combinations for their capacity to increase drought tolerance, (2) test the function and power of our platform to screen the performance of transgenic plants growing in competition, and (3) develop and test an imaging and analysis process as a means of obtaining additional, non-destructive data on plant growth throughout the whole growth cycle instead of relying solely on destructive sampling at the end of the season. The results showed that several transgenic lines under well watered conditions had higher biomass and/or grain weight than the wild-type control but the advantage was significant in one case only. None of the transgenics seemed to show any grain weight advantage under drought stress and only two lines had a substantially but not significantly higher biomass weight than the wild type. However, their evaluation under drought stress was disadvantaged by their delayed flowering date, which increased the drought stress they experienced in comparison to the wild type. Continuous imaging during the season provided additional and non-destructive phenotyping information on the canopy development of mini-plots in our phenotyping platform. A correlation analysis of daily canopy coverage data with harvest metrics showed that the best predictive value from canopy coverage data for harvest metrics was achieved with observations from around heading/flowering to early ripening whereas early season observations had only a limited diagnostic value. The result that the biomass/leaf development in the early growth phase has little correlation with biomass or grain yield data questions imaging approaches concentrating only on the early development stage. Refereed/Peer-reviewed

Advances in biotechnology have rendered tracking of quantitative trait loci (QTLs) a much easier task, making phenotyping, and not genotyping, the main bottleneck to integrating quantitative host plant resistance into breeding programs. The relevance of phenotyping methods is conditioned by their ability to predict the performance of a genotype at the field scale. Components of resistance represent the keystone hierarchy level between resistance expression in the field (the breeder's scale) and QTLs (the geneticist's scale). We describe approaches for upscaling processes to identify components of resistance that best predict field resistance, and for decision making for selection in breeding programs. We further highlight avenues for future research considering specific processes: disease transmission, defoliation, disease escape, polyetic processes, and interactions between components of resistance.

The development of the concepts of precision agriculture and viticulture since the last three decades has shown the need to use first 2D image acquisition techniques and dedicated image processing. More and more needs concern now 3D images and information. The main ideas of this chapter is thus to present some innovations of the 3D tools and methods in the agronomic domain. This chapter will particularly focus on two main subjects such as the 3D characterization of crop using Shape from Focus or Structure from Motion techniques and the 3D use for root phenotyping using rhizotron system. Results presented show that 3D information allows to better characterize crucial crop morphometric parameters using proxy-detection or phenotyping methods. resume du livre : Sensor technologies play a large part in modern life as they are present in security systems, digital cameras, smartphones, and motion sensors. While these devices are always evolving, research is being done to further develop this technology to help detect and analyze threats, perform in-depth inspections, and perform tracking services. Developing and Applying Optoelectronics in Machine Vision evaluates emergent research and theoretical concepts in scanning devices and 3D reconstruction technologies being used to measure their environment. Examining the development of the utilization of machine vision practices and research, optoelectronic devices, and sensor technologies, this book is ideally suited for academics, researchers, students, engineers, and technology developers.

Plant density is useful variable that determines the fate of the wheat crop. The most commonly used method for plant density quantification is based on visual counting from ground level. The objective of this study is to develop and evaluate a method for estimating wheat plant density at the emergence stage based on high resolution imagery taken from UAV at very low altitude with application to high throughput phenotyping in field conditions. A Sony ILCE alpha 5100L RGB camera with 24 Mpixels and equipped with a 60 mm focal length lens was flying aboard an hexacopter at 3 to 7 m altitude at about 1 m/s speed. This allows getting ground resolution between 0.20 mm to 0.45 mm, while providing 59-77% overlap between images. The camera was looking with 45 degrees zenith angle in a compass direction perpendicular to the row direction to maximize the cross section viewed of the plants and minimize the effect of the wind created by the rotors. Agisoft photoscan software was then used to derive the position of the cameras for each image. Images were then projected on the ground surface to finally extract subsamples used to estimate the plant density. The extracted images were first classified to separate the green pixels from the background and the rows were then identified and extracted. Finally, image object (group of connected green pixels) was identified on each row and the number of plants they contain was estimated using a Support Vector Machine whose training was optimized using a Particle Swarm Optimization. Three experiments were conducted in Greoux, Avignon and Clermont sites with some variability in the sowing dates, densities, genotypes, flight altitude, and growth stage at the time of the image acquisition. The application of the method on the 270 samples available over the three sites provides a RMSE and relative RMSE on estimates of 34.05 plants/m(2) and 14.31% with a bias of 9.01 plants/m(2). However, differences in performances were observed between the three sites, mostly related to the growth stage at the time of the flight. Plants should have between one to two leaves when images are taken. Further, a specific sensitivity analysis shows that the ground resolution of the images should be better than 0.40 mm. Finally, the repeatability of the method is good especially when images are taken from similar observational geometries. The current limits and possible improvements of the method proposed are finally discussed.

Background Crop species are of increasing interest both for cattle feeding and for bioethanol production. The degradability of the plant material largely depends on the lignification of the tissues, but it also depends on histological features such as the cellular morphology or the relative amount of each tissue fraction. There is therefore a need for high-throughput phenotyping systems that quantify the histology of plant sections. Results We developed custom image processing and an analysis procedure for quantifying the histology of maize stem sections coloured with FASGA staining and digitalised with whole microscopy slide scanners. The procedure results in an automated segmentation of the input images into distinct tissue regions. The size and the fraction area of each tissue region can be quantified, as well as the average coloration within each region. The measured features can discriminate contrasted genotypes and identify changes in histology induced by environmental factors such as water deficit. Conclusions The simplicity and the availability of the software will facilitate the elucidation of the relationships between the chemical composition of the tissues and changes in plant histology. The tool is expected to be useful for the study of large genetic populations, and to better understand the impact of environmental factors on plant histology. Electronic supplementary material The online version of this article (doi:10.1186/s13007-017-0225-z) contains supplementary material, which is available to authorized users.

Drought is a complex stress which elicits a wide variety of plant responses. As such, genetic studies of drought are particularly difficult. Elucidation of the genetic basis of components contributing to drought tolerance is likely to be more tractable than that of overall drought tolerance. Certain of the traits which contribute to drought tolerance in plants and the high-throughput phenotyping techniques available to measure those traits are described in this paper. On the basis of the dynamic nature of drought, plant development, and the resulting stress response, the focus is on non-destructive imaging techniques which allow a temporal resolution and monitoring of the same plants throughout the experiment. Information on the physiological changes in response to drought over time is vital in order to identify and characterize different drought-tolerance mechanisms. High-throughput imaging provides a valuable new tool which allows the dissection of plant responses to drought into a series of component traits.

One of the applications of genomics is to identify genetic markers linked to loci responsible for variation in phenotypic traits, which could be used in breeding programs to select individuals with favorable alleles, particularly at the seedling stage. With this aim, in the framework of the European project FruitBreedomics, we selected five main peach fruit characters and a resistance trait, controlled by major genes with Mendelian inheritance: fruit flesh color Y, fruit skin pubescence G, fruit shape S, sub-acid fruit D, stone adhesion-flesh texture F-M, and resistance to green peach aphid Rm2. They were all previously mapped in Prunus. We then selected three F1 and three F2 progenies segregating for these characters and developed genetic maps of the linkage groups including the major genes, using the single nucleotide polymorphism (SNP) genome-wide scans obtained with the International Peach SNP Consortium (IPSC) 9K SNP array v1. We identified SNPs co-segregating with the characters in all cases. Their positions were in agreement with the known positions of the major genes. The number of SNPs linked to each of these, as well as the size of the physical regions encompassing them, varied depending on the maps. As a result, the number of useful SNPs for marker-assisted selection varied accordingly. As a whole, this study establishes a sound basis for further development of MAS on these characters. Additionally, we also discussed some limitations that were observed regarding the SNP array efficiency., This work has been funded under the EU seventh Framework program by the FruitBreedomics project (FP7-KBBE-2010-265582): Integrated Approach for increasing breeding efficiency in fruit tree crop.

Phenotypic variation in natural populations results from a combination of genetic effects, environmental effects, and gene-by-environment interactions. Despite the vast amount of genomic data becoming available, many pressing questions remain about the nature of genetic mutations that underlie functional variation. We present the results of combining genome-wide association analysis of 41 different phenotypes in ∼5,000 inbred maize lines to analyze patterns of high-resolution genetic association among of 28.9 million single-nucleotide polymorphisms (SNPs) and ∼800,000 copy-number variants (CNVs). We show that genic and intergenic regions have opposite patterns of enrichment, minor allele frequencies, and effect sizes, implying tradeoffs among the probability that a given polymorphism will have an effect, the detectable size of that effect, and its frequency in the population. We also find that genes tagged by GWAS are enriched for regulatory functions and are ∼50% more likely to have a paralog than expected by chance, indicating that gene regulation and gene duplication are strong drivers of phenotypic variation. These results will likely apply to many other organisms, especially ones with large and complex genomes like maize., Author Summary We performed genome-wide association mapping analysis in maize for 41 different phenotypes in order to identify which types of variants are more likely to be important for controlling traits. We took advantage of a large mapping population (roughly 5000 recombinant inbred lines) and nearly 30 million segregating variants to identify ∼4800 variants that were significantly associated with at least one phenotype. While these variants are enriched in genes, most of them occur outside of genes, often in regions where regulatory elements likely lie. We also found a significant enrichment for paralogous (duplicated) genes, implying that functional divergence after gene duplication plays an important role in trait variation. Overall these analyses provide important insight into the unifying patterns of variation in traits across maize, and the results will likely also apply to other organisms with similarly large, complex genomes.

Root systems are critical for water and nutrient acquisition by crops. Current methods measuring root biomass and length are slow and labour-intensive for studying root responses to environmental stresses in the field. Here, we report the development of a method that measures changes in the root DNA concentration in soil and detects root responses to drought in controlled environment and field trials. To allow comparison of soil DNA concentrations from different wheat genotypes, we also developed a procedure for correcting genotypic differences in the copy number of the target DNA sequence. The new method eliminates the need for separation of roots from soil and permits large-scale phenotyping of root responses to drought or other environmental and disease stresses in the field.

Background Flax (Linum usitatissimum L.) is an economically important fiber and oil crop that has been grown for thousands of years. The genome has been recently sequenced and transcriptomics are providing information on candidate genes potentially related to agronomically-important traits. In order to accelerate functional characterization of these genes we have generated a flax EMS mutant population that can be used as a TILLinG (Targeting Induced Local Lesions in Genomes) platform for forward and reverse genetics. Results A population of 4,894 M2 mutant seed families was generated using 3 different EMS concentrations (0.3%, 0.6% and 0.75%) and used to produce M2 plants for subsequent phenotyping and DNA extraction. 10,839 viable M2 plants (4,033 families) were obtained and 1,552 families (38.5%) showed a visual developmental phenotype (stem size and diameter, plant architecture, flower-related). The majority of these families showed more than one phenotype. Mutant phenotype data are organised in a database and can be accessed and searched at UTILLdb (http://urgv.evry.inra.fr/UTILLdb). Preliminary screens were also performed for atypical fiber and seed phenotypes. Genomic DNA was extracted from 3,515 M2 families and eight-fold pooled for subsequent mutant detection by ENDO1 nuclease mis-match cleavage. In order to validate the collection for reverse genetics, DNA pools were screened for two genes coding enzymes of the lignin biosynthesis pathway: Coumarate-3-Hydroxylase (C3H) and Cinnamyl Alcohol Dehydrogenase (CAD). We identified 79 and 76 mutations in the C3H and CAD genes, respectively. The average mutation rate was calculated as 1/41 Kb giving rise to approximately 9,000 mutations per genome. Thirty-five out of the 52 flax cad mutant families containing missense or codon stop mutations showed the typical orange-brown xylem phenotype observed in CAD down-regulated/mutant plants in other species. Conclusions We have developed a flax mutant population that can be used as an efficient forward and reverse genetics tool. The collection has an extremely high mutation rate that enables the detection of large numbers of independant mutant families by screening a comparatively low number of M2 families. The population will prove to be a valuable resource for both fundamental research and the identification of agronomically-important genes for crop improvement in flax.

Background Fruit composition at harvest is strongly dependent on the temperature during the grapevine developmental cycle. This raises serious concerns regarding the sustainability of viticulture and the socio-economic repercussions of global warming for many regions where the most heat-tolerant varieties are already cultivated. Despite recent progress, the direct and indirect effects of temperature on fruit development are far from being understood. Experimental limitations such as fluctuating environmental conditions, intra-cluster heterogeneity and the annual reproductive cycle introduce unquantifiable biases for gene expression and physiological studies with grapevine. In the present study, DRCF grapevine mutants (microvine) were grown under several temperature regimes in duly-controlled environmental conditions. A singly berry selection increased the accuracy of fruit phenotyping and subsequent gene expression analyses. The physiological and transcriptomic responses of five key stages sampled simultaneously at day and nighttime were studied by RNA-seq analysis. Results A total of 674 millions reads were sequenced from all experiments. Analysis of differential expression yielded in a total of 10 788 transcripts modulated by temperature. An acceleration of green berry development under higher temperature was correlated with the induction of several candidate genes linked to cell expansion. High temperatures impaired tannin synthesis and degree of galloylation at the transcriptomic levels. The timing of malate breakdown was delayed to mid-ripening in transgressively cool conditions, revealing unsuspected plasticity of berry primary metabolism. Specific ATPases and malate transporters displayed development and temperature-dependent expression patterns, besides less marked but significant regulation of other genes in the malate pathway. Conclusion The present study represents, to our knowledge the first abiotic stress study performed on a fleshy fruits model using RNA-seq for transcriptomic analysis. It confirms that a careful stage selection and a rigorous control of environmental conditions are needed to address the long-term plasticity of berry development with respect to temperature. Original results revealed temperature-dependent regulation of key metabolic processes in the elaboration of berry composition. Malate breakdown no longer appears as an integral part of the veraison program, but as possibly triggered by an imbalance in cytoplasmic sugar, when efficient vacuolar storage is set on with ripening, in usual temperature conditions. Furthermore, variations in heat shock responsive genes that will be very valuable for further research on temperature adaptation of plants have been evidenced. Electronic supplementary material The online version of this article (doi:10.1186/s12870-016-0850-0) contains supplementary material, which is available to authorized users.