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Canopy light interception determines the amount of energy captured by a crop, and is thus critical to modeling crop growth and yield, and may substantially contribute to the prediction uncertainty of crop growth models (CGMs). We thus analyzed the canopy light interception models of the 26 wheat (Triticum aestivum) CGMs used by the Agricultural Model Intercomparison and Improvement Project (AgMIP). Twenty-one CGMs assume that the light extinction coefficient (K) is constant, varying from 0.37 to 0.80 depending on the model. The other models take into account the illumination conditions and assume either that all green surfaces in the canopy have the same inclination angle ([Formula: see text]) or that [Formula: see text] distribution follows a spherical distribution. These assumptions have not yet been evaluated due to a lack of experimental data. Therefore, we conducted a field experiment with five cultivars with contrasting leaf stature sown at normal and double row spacing, and analyzed [Formula: see text] distribution in the canopies from three-dimensional canopy reconstructions. In all the canopies, [Formula: see text] distribution was well represented by an ellipsoidal distribution. We thus carried out an intercomparison between the light interception models of the AgMIP–Wheat CGMs ensemble and a physically based K model with ellipsoidal leaf angle distribution and canopy clumping ([Formula: see text]). Results showed that the [Formula: see text] model outperformed current approaches under most illumination conditions and that the uncertainty in simulated wheat growth and final grain yield due to light models could be as high as 45%. Therefore, our results call for an overhaul of light interception models in CGMs.

International audience; The extraction of desirable heritable traits for crop improvement from high-throughput phenotyping (HTP) observations remains challenging. We developed a modeling workflow named Digital Plant Phenotyping Platform, D3P, to access crop architectural traits from HTP observations. D3P couples the ADEL (architectural model of development based on L-systems) wheat (Triticum aestivum) model, that describes the time course of the three-dimensional architecture of wheat crops, with simulators of images acquired with HTP sensors. We demonstrated that a sequential assimilation of the green fraction derived from RGB (Red Green Blue) images of the crop into D3P provides accurate estimates of five key parameters (phyllochron, lamina length of the first leaf, rate of elongation of leaf lamina, number of green leaves at the start of leaf senescence and minimum number of green leaves) of the ADEL-Wheat model that drive the time course of green area index and the number of axes with more than three leaves at the end of the tillering period. However, leaf and tiller orientation and inclination characteristics were poorly estimated. D3P was also used to optimize the observational configuration. The results, obtained from in silico experiments conducted on wheat crops at several vegetative stages, showed that the accessible traits could be estimated accurately with observations made at 0{degree sign} and 60{degree sign} zenith view inclination with a temporal frequency of 100 {degree sign}Cd. This illustrates the potential of the proposed holistic approach that integrates all the available information into a consistent system for interpretation. The potential benefits and limitations of the approach are further discussed.

High-throughput phenotyping data acquisition in the field is highly required to provide breeders with a set of accurate and heritable traits for the evaluation of the genotypes performances. An entirely automatic robot was developed, called Phenomobile, able to sample with a 100-200 plots/hour throughput using a 12m long telescopic arm moving from 1m to 4m height. The vehicle is mounted on caterpillars to operate under difficult soil conditions. The measurement heads include LiDARS, high resolution RGB cameras and multispectral cameras used in active mode with flashes. The phenomobile is driven by RTK-GPS with centimetric accuracy. Sensors are automatically triggered according to a predefined mission, and the data stored under standard format for easy processing. Fist results and conclusions are given in this paper.

Jujube is widely cultivated in the semi-arid region of the Loess Plateau in Northwest China due to its high water deficit tolerance. In such an ecologically vulnerable area, it is critical to explore the water consumption processes of key tree species and their responses to driving factors. Sap flow data gathered during a two-year field study in a jujube plantation were analyzed as a surrogate for transpiration measurements. The measured sap flows were related to changes in the soil water content, meteorological factors (the vapor pressure deficit and the level of photosynthetically active radiation), and plant physiological factors (the sap wood area, leaf area and leaf area index). The factors that govern sap flow were found to vary depending on the growing season, and on hourly and daily timescales. The plants' drought tolerance could be predicted based on their peak sap flows and the variation in their sap flow rates at different soil water levels. The sap flow was most strongly affected by the water content of the topmost (0-20 cm) soil layer. Of the studied meteorological factors, the photosynthetically active radiation had a greater effect on sap flow than the vapor pressure deficit. The correlation we found could be applied to predict jujube tree water consumption and assist the design of irrigation scheme.

International audience; Phenotyping is rapidly evolving from a small set of destructive measurement and visual notations of few simple traits to massive characterizations derived from high-throughput non-invasive proximal or remote sensing techniques. Meanwhile functional structural plant modeling (FSPM) integrates the physiological and morphological information from organizational scales to the canopy level. The combination of new phenotyping techniques with FSPMs is expected therefore to estimate a set of FSPMs parameters corresponding to traits of interest. LiDAR (Light Detection And Ranging) is recently exploited for detailed 3D description of the canopy structure, especially over dense canopies with small elements such as wheat and barley. In this work, we propose to use a model-assisted phenotyping approach to improve our understanding of the interaction between laser beam and canopy. It leads us to develop inversion algorithm to retrieve canopy traits. A discrete LiDAR scanning simulator was first developed based on PlantGL, a 3D plant modeling python library. The footprint and the geometrical configuration of the LiDAR are explicitly accounted for. The LiDAR simulator was validated over an artificial crop made of 45 artificial plants. Actual LiDAR measurements were performed over the same scene. Results proved that the simulator generates a 3D point cloud having the same statistical properties as those derived from the actual LiDAR measurements. Then a synthetic experiment was completed to demonstrate the potentials of model assisted phenotyping. 3D wheat canopy scenes were generated with AdelWheat model for two contrasting development stages corresponding to thermal time 500 °C • day and 1500 °C • day across a wide range of combination of the model parameters (242 cases replicated 20 times). The scenes were transformed into 3D point clouds using the LiDAR simulator. A set of 50 independent cases were generated in addition to evaluate the performances of the method. Results demonstrate that emerging properties could be retrieved with a good accuracy from the 3D map including the leaf area index (LAI) (R2 = 0.85 and rRMSE = 6.11%). The retrieval of other parameters will be discussed with due attention to the complexity of the comparison of simulated 3D point clouds with the measured ones.

poster abstract; Plant density is governed by the sowing density and the emergence rate. For a given plant density, the uniformity of plant distribution may significantly impact the competition between plants as well as weeds. Plant density and uniformity is therefore a key factor explaining production, although a number of species are able to compensate low plant densities by a larger development of individual plants during the growth cycle. However, as the lack of dedicated device, manual field counting in wheat crops is still extensively employed as the reference method although tedious and time consuming. Further, very little work has been devoted to document the plant distribution pattern along the row. The main objective of this work is to propose a high-throughput method to estimate plant density and the distribution pattern from high resolution images taken at the emergence stage. Wheat was selected as the material because it is one of the major crops cultivated over the world with relatively high plant density (150-400 seeds·m-2). High-resolution RGB images were captured from ground-based or unmanned aerial vehicle (UAV) platforms over several wheat experiments in France. They covered a large range of densities from 100 to 600 plants·m-2 over various cultivars at Haun stage around 1.5. Computer vision techniques were exploited to develop a pipeline for assessing the plant density and the distribution patterns of plant positions along the row. The method provides a good estimation of the actual plant density with R2» 0.95 and rRMSE » 12% over ground-based imagery while performances degrade only slightly for the UAV imagery with R2» 0.86 and rRMSE »19%. The inter-plant spacing along the row direction was found following a gamma distribution and the positions of the successive plants are independent from each other. The distribution parameters may be retrieved from the images, subsequently characterizing the uniformity of plant distribution along the row direction.

Pear jujube(Ziziphus jujuba Mill.) as a common tree crop in the Loess Plateau region ofChina,but its ecological and economic benefits have failed to be fully realized because of the shortage of water resources.Therefore,improving jujube yield and water use efficiency are key goals.Studies of the water deficit index and water consumption rules can help to design more optimal irrigation systems and improve water use efficiency.In 2010 and 2011,we studied the water consumption of 4-year-old jujube plants subjected to one of four soil water treatments during their flowering and fruit development periods.We analyzed the correlation of water consumption to variation in trunk diameter and established a regression model.Our goal was to determine how pear jujube stem diameter responded to water conditions during the flowering and fruit development periods to determine a suitable water deficit index to use in building irrigation systems.The flowering and fruit development periods of pear jujube are critical water demand periods.Because these two processes define the main period of reproductive growth,they determine the quantity,volume,weight,and yield of fruit.Stem diameter change indexes included daily mean trunk diameter(MTD),daily maximum trunk diameter(MXTD),maximum daily shrinkage in trunk diameter(MDS),and daily growth of stem diameter(DG).The results indicated that(1) the tree water use increased with an increase in soil water content.Regardless of the treatment differences in soil water conditions,the maximum daily water use all occurred during the week following irrigation,and water consumption during the fruit expansion stage was greater than in flowering and fruit set stages.(2) Two years of experimental data proved that the logistic function could precisely express the changes in MTD and MXTD in the critical water demand periods of jujube,so these two indexes were equivalent in terms of assessing jujube trunk diameter growth during the study period,even though the growth rate varied annually and among treatments.(3) Under the high-water treatment,the indices derived from the trunk diameter(MTD,MXTD,MDS,and DG) were not sensitive to water consumption,while in the low-water treatment,MDS had a highly significant correlation with daily water use compared with the other indexes,indicating its potential as an indicator of jujube water consumption under water deficit conditions.The regression model between water consumption and trunk diameter that we established can be used to analyze the sensitivity of stem diameter variation to water deficit and to detect the water consumption status of pear jujube trees.

Background Plant density and its non-uniformity drive the competition among plants as well as with weeds. They need thus to be estimated with small uncertainties accuracy. An optimal sampling method is proposed to estimate the plant density in wheat crops from plant counting and reach a given precision. Results Three experiments were conducted in 2014 resulting in 14 plots across varied sowing density, cultivars and environmental conditions. The coordinates of the plants along the row were measured over RGB high resolution images taken from the ground level. Results show that the spacing between consecutive plants along the row direction are independent and follow a gamma distribution under the varied conditions experienced. A gamma count model was then derived to define the optimal sample size required to estimate plant density for a given precision. Results suggest that measuring the length of segments containing 90 plants will achieve a precision better than 10%, independently from the plant density. This approach appears more efficient than the usual method based on fixed length segments where the number of plants are counted: the optimal length for a given precision on the density estimation will depend on the actual plant density. The gamma count model parameters may also be used to quantify the heterogeneity of plant spacing along the row by exploiting the variability between replicated samples. Results show that to achieve a 10% precision on the estimates of the 2 parameters of the gamma model, 200 elementary samples corresponding to the spacing between 2 consecutive plants should be measured. Conclusions This method provides an optimal sampling strategy to estimate the plant density and quantify the plant spacing heterogeneity along the row.