The two-dimensional transient response of an imperfect bonded circular lined pipeline lying in an elastic infinite medium is investigated.Imperfect boundary conditions between the surrounding elastic rock and the tunn...The two-dimensional transient response of an imperfect bonded circular lined pipeline lying in an elastic infinite medium is investigated.Imperfect boundary conditions between the surrounding elastic rock and the tunnel are modelled with a two-linear-spring design.The novelty of the manuscript consists in studying at the same time transient regimes and imperfect bonded interfaces for simulating the dynamic response of a tunnel embedded in an elastic infinite rock.Wave propagation fields in tunnel and rock are expressed in terms of infinite Bessel and Hankel series.To solve the transient problem,the Laplace transform and the associated Durbin’s algorithm are performed.To exhibit the dynamic responses,influences of various parameters such as the quality of the interface conditions and the thickness of the lining are presented.The dynamic hoop stresses and the solid displacements of both the tunnel and the rock are also proposed.展开更多
Worldwide, scarce water resources and substantial food demands require efficient water use and high yield.This study investigated whether irrigation frequency can be used to adjust soil moisture to increase grain yiel...Worldwide, scarce water resources and substantial food demands require efficient water use and high yield.This study investigated whether irrigation frequency can be used to adjust soil moisture to increase grain yield and water use efficiency(WUE) of high-yield maize under conditions of mulching and drip irrigation.A field experiment was conducted using three irrigation intervals in 2016: 6, 9, and 12 days(labeled D6, D9, and D12) and five irrigation intervals in 2017: 3, 6, 9, 12, and 15 days(D3, D6, D9, D12, and D15).In Xinjiang, an optimal irrigation quota is 540 mm for high-yield maize.The D3, D6, D9, D12, and D15 irrigation intervals gave grain yields of 19.7, 19.1–21.0, 18.8–20.0, 18.2–19.2, and 17.2 Mg ha^-1 and a WUE of 2.48, 2.53–2.80, 2.47–2.63, 2.34–2.45, and 2.08 kg m-3, respectively.Treatment D6 led to the highest soil water storage, but evapotranspiration and soil-water evaporation were lower than other treatments.These results show that irrigation interval D6 can help maintain a favorable soil-moisture environment in the upper-60-cm soil layer, reduce soilwater evaporation and evapotranspiration, and produce the highest yield and WUE.In this arid region and in other regions with similar soil and climate conditions, a similar irrigation interval would thus be beneficial for adjusting soil moisture to increase maize yield and WUE under conditions of mulching and drip irrigation.展开更多
A crop growth model,integrating genotype,environment,and management factor,was developed to serve as an analytical tool to study the influence of these factors on crop growth,production,and agricultural planning.A maj...A crop growth model,integrating genotype,environment,and management factor,was developed to serve as an analytical tool to study the influence of these factors on crop growth,production,and agricultural planning.A major challenge of model application is the optimization and calibration of a considerable number of parameters.Sensitivity analysis(SA) has become an effective method to identify the importance of various parameters.In this study,the extended Fourier Amplitude Sensitivity Test(EFAST) approach was used to evaluate the sensitivity of the DSSAT-CERES model output responses of interest to 39 crop genotype parameters and six soil parameters.The outputs for the SA included grain yield and quality(take grain protein content(GPC) as an indicator) at maturity stage,as well as leaf area index,aboveground biomass,and aboveground nitrogen accumulation at the critical process variables.The key results showed that:(1) the influence of parameter bounds on the sensitivity results was slight and less than the impacts from the significance of the parameters themselves;(2) the sensitivity parameters of grain yield and GPC were different,and the sensitivity of the interactions between parameters to GPC was greater than those between the parameters to grain yield;and(3) the sensitivity analyses of some process variables,including leaf area index,aboveground biomass,and aboveground nitrogen accumulation,should be performed differently.Finally,some parameters,which improve the model’s structure and the accuracy of the process simulation,should not be ignored when maturity output as an objective variable is studied.展开更多
Post-sedimentary transformations have masked or completely obliterated the structures and textures of Precambrian carbonate rocks. Therefore, methods of classification of the carbonate rocks founded on the observation...Post-sedimentary transformations have masked or completely obliterated the structures and textures of Precambrian carbonate rocks. Therefore, methods of classification of the carbonate rocks founded on the observation of primary structures or textural characteristics are ill-adapted. Consequently, only certain geochemical classification methods allow us to distinguish the various rock-types in the case of Neoproterozoic carbonates. After presenting the most suitable geochemical classifications, we propose a new classification into 14 groups based on a regular ternary diagram with computerized data input. For each sample of carbonate rock, analysis of calcium and magnesium contents allows us to calculate the input data for our diagram i.e. the percentages of Calcite, Dolomite and Insoluble Residue. To automate the application of this diagram, input parameters are created in a descriptive file “Roches.ternaires.txt” using an option called “Ternaires” in the “Diagrammes” software developed by Roland Simler. Thirty cap carbonates of Africa are used to validate this new method.展开更多
The use of a crop model like STICS for appropriate management decision support requires a good knowledge of all the parameters of the model. Among them, the soil parameters are difficult to know at each point of inter...The use of a crop model like STICS for appropriate management decision support requires a good knowledge of all the parameters of the model. Among them, the soil parameters are difficult to know at each point of interest and costly techniques may be used to measure them. It is therefore important to know which soil parameters need to be determined. It can be stated that those which affect significantly the output variable deserve an accurate determination while those which slightly affect the model output variable do not. This paper demonstrates how a global sensitivity analysis method based on variance decomposition can be applied on soil parameters in order to divide them in the two categories. The Extended FAST method applied to the crop model STICS and a set of 13 soil parameters first allows to calculate the part of variance explained by each soil parameter (giving global sensitivity indices of the soil parameters) and the coefficient of variation of the output variables (measuring the effect of the parameter uncertainty on each variable). These metrics are therefore used for deciding on the importance of the parameter value measurement. Different output variables (Leaf Area Index and chlorophyll content) are evaluated at different stages of interest while others (crop yield, grain protein content, soil mineral nitrogen) are evaluated at harvest. The analysis is applied on two different annual crops (wheat and sugar beet), two contrasted weather and two types of soil depth. When the uncertainty of the output generated by the soil parameters is large (coefficient of variation > 1/3), only the parameters having a significant global sensitivity indices (higher than 10%) are retained. The results show that the number of soil parameters which deserve an accurate determination can be significantly reduced by the use of this relevant method for appropriate management decision support.展开更多
Hydrogeological modeling is an interesting and widely-used approach to improving our understanding of groundwater,both to test existing hypotheses on the behavior of hydrosystems and to predict their responses to vari...Hydrogeological modeling is an interesting and widely-used approach to improving our understanding of groundwater,both to test existing hypotheses on the behavior of hydrosystems and to predict their responses to various natural or man-made problems.Today,software such as Leapfrog Geo offers the possibility of building a 3D geological model with a more accurate representation of the subsurface.Statistical tools such as ordinary kriging can be used to simulate the spatial distribution of groundwater.These modeling approaches were combined to improve knowledge of the groundwater flow context within three massifs on the island of Grande Comore.The delineation of the 3D geometry of litho-stratigraphic units has enabled a more detailed conceptualization of groundwater flows in a complex volcanic environment.Piezometric interpolations were used to validate aquifer geometry.It has been demonstrated that an implicit geological model coupled with piezometry can provide very interesting information on the hydrogeological configuration of a volcanic massif.In the Karthala and Badjini massifs,the respective confined and semi-confined configurations of the aquifers are observed,with thicknesses that progressively decrease with distance from the coast.In the Grille massif,on the other hand,the aquifer configuration is unconfined,with thickness increasing with distance from the coast.In all three massifs,the flow of water in the underground hydrosystems is from the central part towards the coast,naturally following the geological configuration of the ground.It should be noted that the absence of data in the central parts of the massifs still leaves uncertainties about the geometry in these parts of the aquifers.However,the models that have been established provide valid hypotheses for characterizing the hydrogeological configuration at the scale of each massif.展开更多
Computer vision is increasingly used in farmers'fields and agricultural experiments to quantify important traits.Imaging setups with a sub-millimeter ground sampling distance enable the detection and tracking of p...Computer vision is increasingly used in farmers'fields and agricultural experiments to quantify important traits.Imaging setups with a sub-millimeter ground sampling distance enable the detection and tracking of plant features,including size,shape,and colour.Although today's AI-driven foundation models segment almost any object in an image,they still fail for complex plant canopies.To improve model performance,the global wheat dataset consortium assembled a diverse set of images from experiments around the globe.After the head detection dataset(GWHD),the new dataset targets a full semantic segmentation(GWFSS)of organs(leaves,stems and spikes)covering all developmental stages.Images were collected by 11 institutions using a wide range of imaging setups.Two datasets are provided:ⅰ)a set of 1096 diverse images in which all organs were labelled at the pixel level,and(ⅱ)a dataset of 52,078 images without annotations available for additional training.The labelled set was used to train segmentation models based on DeepLabV3Plus and Segformer.Our Segformer model performed slightly better than DeepLabV3Plus with a mIOU for leaves and spikes of ca.90%.However,the precision for stems with 54%was rather lower.The major advantages over published models are:ⅰ)the exclusion of weeds from the wheat canopy,ⅱ)the detection of all wheat features including necrotic and se-nescent tissues and its separation from crop residues.This facilitates further development in classifying healthy vs.unhealthy tissue to address the increasing need for accurate quantification of senescence and diseases in wheat canopies.展开更多
Total above-ground biomass at harvest and ear density are two important traits that characterize wheat genotypes.Two experiments were carried out in two different sites where several genotypes were grown under contras...Total above-ground biomass at harvest and ear density are two important traits that characterize wheat genotypes.Two experiments were carried out in two different sites where several genotypes were grown under contrasted irrigation and nitrogen treatments.A high spatial resolution RGB camera was used to capture the residual stems standing straight after the cutting by the combine machine during harvest.It provided a ground spatial resolution better than 0.2 mm.A Faster Regional Convolutional Neural Network(Faster-RCNN)deep-learning model was first trained to identify the stems cross section.Results showed that the identification provided precision and recall close to 95%.Further,the balance between precision and recall allowed getting accurate estimates of the stem density with a relative RMSE close to 7%and robustness across the two experimental sites.The estimated stem density was also compared with the ear density measured in the field with traditional methods.A very high correlation was found with almost no bias,indicating that the stem density could be a good proxy of the ear density.The heritability/repeatability evaluated over 16 genotypes in one of the two experiments was slightly higher(80%)than that of the ear density(78%).The diameter of each stem was computed from the profile of gray values in the extracts of the stem cross section.Results show that the stem diameters follow a gamma distribution over eachmicroplot with an average diameter close to 2.0mm.Finally,the biovolume computed as the product of the average stem diameter,the stem density,and plant height is closely related to the above-ground biomass at harvest with a relative RMSE of 6%.Possible limitations of the findings and future applications are finally discussed.展开更多
Selection of sugar beet(Beta vulgaris L.)cultivars that are resistant to Cercospora Leaf Spot(CLS)disease is critical to increase yield.Such selection requires an automatic,fast,and objective method to assess CLS seve...Selection of sugar beet(Beta vulgaris L.)cultivars that are resistant to Cercospora Leaf Spot(CLS)disease is critical to increase yield.Such selection requires an automatic,fast,and objective method to assess CLS severity on thousands of cultivars in the field.For this purpose,we compare the use of submillimeter scale RGB imagery acquired from an Unmanned Ground Vehicle(UGV)under active illumination and centimeter scale multispectral imagery acquired from an Unmanned Aerial Vehicle(UAV)under passive illumination.Several variables are extracted from the images(spot density and spot size for UGV,green fraction for UGV and UAV)and related to visual scores assessed by an expert.Results show that spot density and green fraction are critical variables to assess low and high CLS severities,respectively,which emphasizes the importance of having submillimeter images to early detect CLS in field conditions.Genotype sensitivity to CLS can then be accurately retrieved based on time integrals of UGV-and UAV-derived scores.While UGV shows the best estimation performance,UAV can show accurate estimates of cultivar sensitivity if the data are properly acquired.Advantages and limitations of UGV,UAV,and visual scoring methods are finally discussed in the perspective of high-throughput phenotyping.展开更多
Early-stage plant density is an essential trait that determines the fate of a genotype under given environmental conditions and management practices.The use of RGB images taken from UAVs may replace the traditional vi...Early-stage plant density is an essential trait that determines the fate of a genotype under given environmental conditions and management practices.The use of RGB images taken from UAVs may replace the traditional visual counting in fields with improved throughput,accuracy,and access to plant localization.However,high-resolution images are required to detect the small plants present at the early stages.This study explores the impact of image ground sampling distance(GSD)on the performances of maize plant detection at three-to-five leaves stage using Faster-RCNN object detection algorithm.Data collected at high resolution(GSD≈0:3 cm)over six contrasted sites were used for model training.Two additional sites with images acquired both at high and low(GSD≈0:6 cm)resolutions were used to evaluate the model performances.Results show that Faster-RCNN achieved very good plant detection and counting(rRMSE=0:08)performances when native high-resolution images are used both for training and validation.Similarly,good performances were observed(rRMSE=0:11)when the model is trained over synthetic low-resolution images obtained by downsampling the native training high-resolution images and applied to the synthetic low-resolution validation images.Conversely,poor performances are obtained when the model is trained on a given spatial resolution and applied to another spatial resolution.Training on a mix of high-and low-resolution images allows to get very good performances on the native high-resolution(rRMSE=0:06)and synthetic low-resolution(rRMSE=0:10)images.However,very low performances are still observed over the native low-resolution images(rRMSE=0:48),mainly due to the poor quality of the native low-resolution images.Finally,an advanced super resolution method based on GAN(generative adversarial network)that introduces additional textural information derived from the native high-resolution images was applied to the native low-resolution validation images.Results show some significant improvement(rRMSE=0:22)compared to bicubic upsampling approach,while still far below the performances achieved over the native high-resolution images.展开更多
Multispectral observations from unmanned aerial vehicles(UAVs)are currently used for precision agriculture and crop phenotyping applications to monitor a series of traits allowing the characterization of the vegetatio...Multispectral observations from unmanned aerial vehicles(UAVs)are currently used for precision agriculture and crop phenotyping applications to monitor a series of traits allowing the characterization of the vegetation status.However,the limited autonomy of UAVs makes the completion of flights difficult when sampling large areas.Increasing the throughput of data acquisition while not degrading the ground sample distance(GSD)is,therefore,a critical issue to be solved.We propose here a new image acquisition configuration based on the combination of two focal length(f)optics:an optics with f=4:2 mm is added to the standard f=8 mm(SS:single swath)of the multispectral camera(DS:double swath,double of the standard one).Two flights were completed consecutively in 2018 over a maize field using the AIRPHEN multispectral camera at 52 m altitude.The DS flight plan was designed to get 80%overlap with the 4.2 mm optics,while the SS one was designed to get 80%overlap with the 8 mm optics.As a result,the time required to cover the same area is halved for the DS as compared to the SS.The georeferencing accuracy was improved for the DS configuration,particularly for the Z dimension due to the larger view angles available with the small focal length optics.Application to plant height estimates demonstrates that the DS configuration provides similar results as the SS one.However,for both the DS and SS configurations,degrading the quality level used to generate the 3D point cloud significantly decreases the plant height estimates.展开更多
The sowing pattern has an important impact on light interception efficiency in maize by determining the spatial distribution of leaves within the canopy.Leaves orientation is an important architectural trait determini...The sowing pattern has an important impact on light interception efficiency in maize by determining the spatial distribution of leaves within the canopy.Leaves orientation is an important architectural trait determining maize canopies light interception.Previous studies have indicated how maize genotypes may adapt leaves orientation to avoid mutual shading with neighboring plants as a plastic response to intraspecific competition.The goal of the present study is 2-fold:firstly,to propose and validate an automatic algorithm(Automatic Leaf Azimuth Estimation from Midrib detection[ALAEM])based on leaves midrib detection in vertical red green blue(RGB)images to describe leaves orientation at the canopy level;and secondly,to describe genotypic and environmental differences in leaves orientation in a panel of 5 maize hybrids sowing at 2 densities(6 and 12 plants.m^(−2))and 2 row spacing(0.4 and 0.8 m)over 2 different sites in southern France.The ALAEM algorithm was validated against in situ annotations of leaves orientation,showing a satisfactory agreement(root mean square[RMSE]error=0.1,R^(2)=0.35)in the proportion of leaves oriented perpendicular to rows direction across sowing patterns,genotypes,and sites.The results from ALAEM permitted to identify significant differences in leaves orientation associated to leaves intraspecific competition.In both experiments,a progressive increase in the proportion of leaves oriented perpendicular to the row is observed when the rectangularity of the sowing pattern increases from 1(6 plants.m^(−2),0.4 m row spacing)towards 8(12 plants.m^(−2),0.8 m row spacing).Significant differences among the 5 cultivars were found,with 2 hybrids exhibiting,systematically,a more plastic behavior with a significantly higher proportion of leaves oriented perpendicularly to avoid overlapping with neighbor plants at high rectangularity.Differences in leaves orientation were also found between experiments in a squared sowing pattern(6 plants.m^(−2),0.4 m row spacing),indicating a possible contribution of illumination conditions inducing a preferential orientation toward east-west direction when intraspecific competition is low.展开更多
The number of leaves at a given time is important to characterize plant growth and development.In this work,we developed a high-throughput method to count the number of leaves by detecting leaf tips in RGB images.The ...The number of leaves at a given time is important to characterize plant growth and development.In this work,we developed a high-throughput method to count the number of leaves by detecting leaf tips in RGB images.The digital plant phenotyping platform was used to simulate a large and diverse dataset of RGB images and corresponding leaf tip labels of wheat plants at seedling stages(150,000 images with over 2 million labels).The realism of the images was then improved using domain adaptation methods before training deep learning models.The results demonstrate the efficiency of the proposed method evaluated on a diverse test dataset,collecting measurements from 5 countries obtained under different environments,growth stages,and lighting conditions with different cameras(450 images with over 2,162 labels).Among the 6 combinations of deep learning models and domain adaptation techniques,the Faster-RCNN model with cycle-consistent generative adversarial network adaptation technique provided the best performance(R^(2)=0.94,root mean square error=8.7).Complementary studies show that it is essential to simulate images with sufficient realism(background,leaf texture,and lighting conditions)before applying domain adaptation techniques.Furthermore,the spatial resolution should be better than 0.6 mm per pixel to identify leaf tips.The method is claimed to be self-supervised since no manual labeling is required for model training.The self-supervised phenotyping approach developed here offers great potential for addressing a wide range of plant phenotyping problems.The trained networks are available at https://github.com/YinglunLi/Wheat-leaf-tip-detection.展开更多
The MODIS LAI/FPAR products have been widely used in various fields since their first public release in 2000.This review intends to summarize the history,development trends,scientific collaborations,disciplines involv...The MODIS LAI/FPAR products have been widely used in various fields since their first public release in 2000.This review intends to summarize the history,development trends,scientific collaborations,disciplines involved,and research hotspots of these products.Its aim is to intrigue researchers and stimulate new research direction.Based on literature data from the Web of Science(WOS)and associated funding information,we conducted a bibliometric visualization review of the MODIS LAI/FPAR products from 1995 to 2020 using bibliometric and social network analysis(SNA)methods.We drew the following conclusions:(1)research based on the MODIS LAI/FPAR shows an upward trend with a multiyear average growth rate of 24.9%in the number of publications.(2)Researchers from China and the USA are the backbone of this research area,among which the Chinese Academy of Sciences(CAS)is the core research institution.(3)Research based on the MODIS LAI/FPAR covers a wide range of disciplines but mainly focus on environmental science and ecology.(4)Ecology,crop production estimation,algorithm improvement,and validation are the hotspots of these studies.(5)Broadening the research field,improving the algorithms,and overcoming existing difficulties in heterogeneous surface,scale effects,and complex terrains will be the trend of future research.Our work provides a clear view of the development of the MODIS LAI/FPAR products and valuable information for scholars to broaden their research fields.展开更多
Plant growth rhythm in structural traits is important for better understanding plant response to the ever-changing environment.Terrestrial laser scanning(TLS)is a well-suited tool to study structural rhythm under fiel...Plant growth rhythm in structural traits is important for better understanding plant response to the ever-changing environment.Terrestrial laser scanning(TLS)is a well-suited tool to study structural rhythm under field conditions.Recent studies have used TLS to describe the structural rhythm of trees,but no consistent patterns have been drawn.Meanwhile,whether TLS can capture structural rhythm in crops is unclear.Here,we aim to explore the seasonal and circadian rhythms in maize structural traits at both the plant and leaf levels from time-series TLS.The seasonal rhythm was studied using TLS data collected at four key growth periods,including jointing,bell-mouthed,heading,and maturity periods.Circadian rhythms were explored by using TLS data acquired around every 2 hours in a whole day under standard and cold stress conditions.Results showed that TLS can quantify the seasonal and circadian rhythm in structural traits at both plant and leaf levels.(1)Leaf inclination angle decreased significantly between the jointing stage and bell-mouthed stage.Leaf azimuth was stable after the jointing stage.(2)Some individual-level structural rhythms(e.g.,azimuth and projected leaf area/PLA)were consistent with leaf-level structural rhythms.(3)The circadian rhythms of some traits(e.g.,PLA)were not consistent under standard and cold stress conditions.(4)Environmental factors showed better correlations with leaf traits under cold stress than standard conditions.Temperature was the most important factor that significantly correlated with all leaf traits except leaf azimuth.This study highlights the potential of time-series TLS in studying outdoor agricultural chronobiology.展开更多
The detection of wheat heads in plant images is an important task for estimating pertinent wheat traits including head population density and head characteristics such as health,size,maturity stage,and the presence of...The detection of wheat heads in plant images is an important task for estimating pertinent wheat traits including head population density and head characteristics such as health,size,maturity stage,and the presence of awns.Several studies have developed methods for wheat head detection from high-resolution RGB imagery based on machine learning algorithms.However,these methods have generally been calibrated and validated on limited datasets.High variability in observational conditions,genotypic differences,development stages,and head orientation makes wheat head detection a challenge for computer vision.Further,possible blurring due to motion or wind and overlap between heads for dense populations make this task even more complex.Through a joint international collaborative effort,we have built a large,diverse,and well-labelled dataset of wheat images,called the Global Wheat Head Detection(GWHD)dataset.It contains 4700 high-resolution RGB images and 190000 labelled wheat heads collected from several countries around the world at different growth stages with a wide range of genotypes.Guidelines for image acquisition,associating minimum metadata to respect FAIR principles,and consistent head labelling methods are proposed when developing new head detection datasets.The GWHD dataset is publicly available at http://www.global-wheat.com/and aimed at developing and benchmarking methods for wheat head detection.展开更多
Data competitions have become a popular approach to crowdsource new data analysis methods for general and specialized data science problems.Data competitions have a rich history in plant phenotyping,and new outdoor fi...Data competitions have become a popular approach to crowdsource new data analysis methods for general and specialized data science problems.Data competitions have a rich history in plant phenotyping,and new outdoor field datasets have the potential to embrace solutions across research and commercial applications.We developed the Global Wheat Challenge as a generalization competition in 2020 and 2021 to find more robust solutions for wheat head detection using field images from different regions.We analyze the winning challenge solutions in terms of their robustness when applied to new datasets.We found that the design of the competition had an influence on the selection of winning solutions and provide recommendations for future competitions to encourage the selection of more robust solutions.展开更多
The strong societal demand to reduce pesticide use and adaptation to climate change challenges the capacities of phenotyping new varieties in the vineyard.High-throughput phenotyping is a way to obtain meaningful and ...The strong societal demand to reduce pesticide use and adaptation to climate change challenges the capacities of phenotyping new varieties in the vineyard.High-throughput phenotyping is a way to obtain meaningful and reliable information on hundreds of genotypes in a limited period.We evaluated traits related to growth in 209 genotypes from an interspecific grapevine biparental cross,between IJ119,a local genitor,and Divona,both in summer and in winter,using several methods:fresh pruning wood weight,exposed leaf area calculated from digital images,leaf chlorophyll concentration,and LiDAR-derived apparent volumes.Using high-density genetic information obtained by the genotyping by sequencing technology(GBS),we detected 6 regions of the grapevine genome[quantitative trait loci(QTL)]associated with the variations of the traits in the progeny.The detection of statistically significant QTLs,as well as correlations(R^(2))with traditional methods above 0.46,shows that LiDAR technology is effective in characterizing the growth features of the grapevine.Heritabilities calculated with LiDAR-derived total canopy and pruning wood volumes were high,above 0.66,and stable between growing seasons.These variables provided genetic models explaining up to 47%of the phenotypic variance,which were better than models obtained with the exposed leaf area estimated from images and the destructive pruning weight measurements.Our results highlight the relevance of LiDAR-derived traits for characterizing genetically induced differences in grapevine growth and open new perspectives for high-throughput phenotyping of grapevines in the vineyard.展开更多
文摘The two-dimensional transient response of an imperfect bonded circular lined pipeline lying in an elastic infinite medium is investigated.Imperfect boundary conditions between the surrounding elastic rock and the tunnel are modelled with a two-linear-spring design.The novelty of the manuscript consists in studying at the same time transient regimes and imperfect bonded interfaces for simulating the dynamic response of a tunnel embedded in an elastic infinite rock.Wave propagation fields in tunnel and rock are expressed in terms of infinite Bessel and Hankel series.To solve the transient problem,the Laplace transform and the associated Durbin’s algorithm are performed.To exhibit the dynamic responses,influences of various parameters such as the quality of the interface conditions and the thickness of the lining are presented.The dynamic hoop stresses and the solid displacements of both the tunnel and the rock are also proposed.
基金research support from the National Key Research and Development Program of China (2016YFD0300110, 2016YFD0300101)the National Basic Research Program of China (2015CB150401)+2 种基金the National Natural Science Foundation of China (31360302)the Science and Technology Program of the Sixth Division of Xinjiang Construction Corps in China (1703)the Agricultural Science and Technology Innovation Program for financial support.
文摘Worldwide, scarce water resources and substantial food demands require efficient water use and high yield.This study investigated whether irrigation frequency can be used to adjust soil moisture to increase grain yield and water use efficiency(WUE) of high-yield maize under conditions of mulching and drip irrigation.A field experiment was conducted using three irrigation intervals in 2016: 6, 9, and 12 days(labeled D6, D9, and D12) and five irrigation intervals in 2017: 3, 6, 9, 12, and 15 days(D3, D6, D9, D12, and D15).In Xinjiang, an optimal irrigation quota is 540 mm for high-yield maize.The D3, D6, D9, D12, and D15 irrigation intervals gave grain yields of 19.7, 19.1–21.0, 18.8–20.0, 18.2–19.2, and 17.2 Mg ha^-1 and a WUE of 2.48, 2.53–2.80, 2.47–2.63, 2.34–2.45, and 2.08 kg m-3, respectively.Treatment D6 led to the highest soil water storage, but evapotranspiration and soil-water evaporation were lower than other treatments.These results show that irrigation interval D6 can help maintain a favorable soil-moisture environment in the upper-60-cm soil layer, reduce soilwater evaporation and evapotranspiration, and produce the highest yield and WUE.In this arid region and in other regions with similar soil and climate conditions, a similar irrigation interval would thus be beneficial for adjusting soil moisture to increase maize yield and WUE under conditions of mulching and drip irrigation.
基金supported by the National Natural Science Foundation of China(41701375,41601369,and 41471285)the European Space Agency(ESA)and Ministry of Science and Technology of China(MOST)Dragon 4 Cooperation Programme(32275-1)
文摘A crop growth model,integrating genotype,environment,and management factor,was developed to serve as an analytical tool to study the influence of these factors on crop growth,production,and agricultural planning.A major challenge of model application is the optimization and calibration of a considerable number of parameters.Sensitivity analysis(SA) has become an effective method to identify the importance of various parameters.In this study,the extended Fourier Amplitude Sensitivity Test(EFAST) approach was used to evaluate the sensitivity of the DSSAT-CERES model output responses of interest to 39 crop genotype parameters and six soil parameters.The outputs for the SA included grain yield and quality(take grain protein content(GPC) as an indicator) at maturity stage,as well as leaf area index,aboveground biomass,and aboveground nitrogen accumulation at the critical process variables.The key results showed that:(1) the influence of parameter bounds on the sensitivity results was slight and less than the impacts from the significance of the parameters themselves;(2) the sensitivity parameters of grain yield and GPC were different,and the sensitivity of the interactions between parameters to GPC was greater than those between the parameters to grain yield;and(3) the sensitivity analyses of some process variables,including leaf area index,aboveground biomass,and aboveground nitrogen accumulation,should be performed differently.Finally,some parameters,which improve the model’s structure and the accuracy of the process simulation,should not be ignored when maturity output as an objective variable is studied.
文摘Post-sedimentary transformations have masked or completely obliterated the structures and textures of Precambrian carbonate rocks. Therefore, methods of classification of the carbonate rocks founded on the observation of primary structures or textural characteristics are ill-adapted. Consequently, only certain geochemical classification methods allow us to distinguish the various rock-types in the case of Neoproterozoic carbonates. After presenting the most suitable geochemical classifications, we propose a new classification into 14 groups based on a regular ternary diagram with computerized data input. For each sample of carbonate rock, analysis of calcium and magnesium contents allows us to calculate the input data for our diagram i.e. the percentages of Calcite, Dolomite and Insoluble Residue. To automate the application of this diagram, input parameters are created in a descriptive file “Roches.ternaires.txt” using an option called “Ternaires” in the “Diagrammes” software developed by Roland Simler. Thirty cap carbonates of Africa are used to validate this new method.
文摘The use of a crop model like STICS for appropriate management decision support requires a good knowledge of all the parameters of the model. Among them, the soil parameters are difficult to know at each point of interest and costly techniques may be used to measure them. It is therefore important to know which soil parameters need to be determined. It can be stated that those which affect significantly the output variable deserve an accurate determination while those which slightly affect the model output variable do not. This paper demonstrates how a global sensitivity analysis method based on variance decomposition can be applied on soil parameters in order to divide them in the two categories. The Extended FAST method applied to the crop model STICS and a set of 13 soil parameters first allows to calculate the part of variance explained by each soil parameter (giving global sensitivity indices of the soil parameters) and the coefficient of variation of the output variables (measuring the effect of the parameter uncertainty on each variable). These metrics are therefore used for deciding on the importance of the parameter value measurement. Different output variables (Leaf Area Index and chlorophyll content) are evaluated at different stages of interest while others (crop yield, grain protein content, soil mineral nitrogen) are evaluated at harvest. The analysis is applied on two different annual crops (wheat and sugar beet), two contrasted weather and two types of soil depth. When the uncertainty of the output generated by the soil parameters is large (coefficient of variation > 1/3), only the parameters having a significant global sensitivity indices (higher than 10%) are retained. The results show that the number of soil parameters which deserve an accurate determination can be significantly reduced by the use of this relevant method for appropriate management decision support.
文摘Hydrogeological modeling is an interesting and widely-used approach to improving our understanding of groundwater,both to test existing hypotheses on the behavior of hydrosystems and to predict their responses to various natural or man-made problems.Today,software such as Leapfrog Geo offers the possibility of building a 3D geological model with a more accurate representation of the subsurface.Statistical tools such as ordinary kriging can be used to simulate the spatial distribution of groundwater.These modeling approaches were combined to improve knowledge of the groundwater flow context within three massifs on the island of Grande Comore.The delineation of the 3D geometry of litho-stratigraphic units has enabled a more detailed conceptualization of groundwater flows in a complex volcanic environment.Piezometric interpolations were used to validate aquifer geometry.It has been demonstrated that an implicit geological model coupled with piezometry can provide very interesting information on the hydrogeological configuration of a volcanic massif.In the Karthala and Badjini massifs,the respective confined and semi-confined configurations of the aquifers are observed,with thicknesses that progressively decrease with distance from the coast.In the Grille massif,on the other hand,the aquifer configuration is unconfined,with thickness increasing with distance from the coast.In all three massifs,the flow of water in the underground hydrosystems is from the central part towards the coast,naturally following the geological configuration of the ground.It should be noted that the absence of data in the central parts of the massifs still leaves uncertainties about the geometry in these parts of the aquifers.However,the models that have been established provide valid hypotheses for characterizing the hydrogeological configuration at the scale of each massif.
基金Global wheat was directly supported by Analytics for the Australian Grains Industry(AAGI).
文摘Computer vision is increasingly used in farmers'fields and agricultural experiments to quantify important traits.Imaging setups with a sub-millimeter ground sampling distance enable the detection and tracking of plant features,including size,shape,and colour.Although today's AI-driven foundation models segment almost any object in an image,they still fail for complex plant canopies.To improve model performance,the global wheat dataset consortium assembled a diverse set of images from experiments around the globe.After the head detection dataset(GWHD),the new dataset targets a full semantic segmentation(GWFSS)of organs(leaves,stems and spikes)covering all developmental stages.Images were collected by 11 institutions using a wide range of imaging setups.Two datasets are provided:ⅰ)a set of 1096 diverse images in which all organs were labelled at the pixel level,and(ⅱ)a dataset of 52,078 images without annotations available for additional training.The labelled set was used to train segmentation models based on DeepLabV3Plus and Segformer.Our Segformer model performed slightly better than DeepLabV3Plus with a mIOU for leaves and spikes of ca.90%.However,the precision for stems with 54%was rather lower.The major advantages over published models are:ⅰ)the exclusion of weeds from the wheat canopy,ⅱ)the detection of all wheat features including necrotic and se-nescent tissues and its separation from crop residues.This facilitates further development in classifying healthy vs.unhealthy tissue to address the increasing need for accurate quantification of senescence and diseases in wheat canopies.
基金This study was supported by “Programme d'Investissementd'Avenir” PHENOME(ANR-11-INBS-012)with participation of FranceAgriMer and “Fonds de Soutien a I'Obtention Vegetale”.
文摘Total above-ground biomass at harvest and ear density are two important traits that characterize wheat genotypes.Two experiments were carried out in two different sites where several genotypes were grown under contrasted irrigation and nitrogen treatments.A high spatial resolution RGB camera was used to capture the residual stems standing straight after the cutting by the combine machine during harvest.It provided a ground spatial resolution better than 0.2 mm.A Faster Regional Convolutional Neural Network(Faster-RCNN)deep-learning model was first trained to identify the stems cross section.Results showed that the identification provided precision and recall close to 95%.Further,the balance between precision and recall allowed getting accurate estimates of the stem density with a relative RMSE close to 7%and robustness across the two experimental sites.The estimated stem density was also compared with the ear density measured in the field with traditional methods.A very high correlation was found with almost no bias,indicating that the stem density could be a good proxy of the ear density.The heritability/repeatability evaluated over 16 genotypes in one of the two experiments was slightly higher(80%)than that of the ear density(78%).The diameter of each stem was computed from the profile of gray values in the extracts of the stem cross section.Results show that the stem diameters follow a gamma distribution over eachmicroplot with an average diameter close to 2.0mm.Finally,the biovolume computed as the product of the average stem diameter,the stem density,and plant height is closely related to the above-ground biomass at harvest with a relative RMSE of 6%.Possible limitations of the findings and future applications are finally discussed.
基金The authors would like to thank Catherine Zanotto and Mathieu Hemmerléfor their help in the experiments.This work was supported by the French National Research Agency in the framework of the“Investissements d’avenir”program AKER(ANR-11-BTBR-0007).
文摘Selection of sugar beet(Beta vulgaris L.)cultivars that are resistant to Cercospora Leaf Spot(CLS)disease is critical to increase yield.Such selection requires an automatic,fast,and objective method to assess CLS severity on thousands of cultivars in the field.For this purpose,we compare the use of submillimeter scale RGB imagery acquired from an Unmanned Ground Vehicle(UGV)under active illumination and centimeter scale multispectral imagery acquired from an Unmanned Aerial Vehicle(UAV)under passive illumination.Several variables are extracted from the images(spot density and spot size for UGV,green fraction for UGV and UAV)and related to visual scores assessed by an expert.Results show that spot density and green fraction are critical variables to assess low and high CLS severities,respectively,which emphasizes the importance of having submillimeter images to early detect CLS in field conditions.Genotype sensitivity to CLS can then be accurately retrieved based on time integrals of UGV-and UAV-derived scores.While UGV shows the best estimation performance,UAV can show accurate estimates of cultivar sensitivity if the data are properly acquired.Advantages and limitations of UGV,UAV,and visual scoring methods are finally discussed in the perspective of high-throughput phenotyping.
文摘Early-stage plant density is an essential trait that determines the fate of a genotype under given environmental conditions and management practices.The use of RGB images taken from UAVs may replace the traditional visual counting in fields with improved throughput,accuracy,and access to plant localization.However,high-resolution images are required to detect the small plants present at the early stages.This study explores the impact of image ground sampling distance(GSD)on the performances of maize plant detection at three-to-five leaves stage using Faster-RCNN object detection algorithm.Data collected at high resolution(GSD≈0:3 cm)over six contrasted sites were used for model training.Two additional sites with images acquired both at high and low(GSD≈0:6 cm)resolutions were used to evaluate the model performances.Results show that Faster-RCNN achieved very good plant detection and counting(rRMSE=0:08)performances when native high-resolution images are used both for training and validation.Similarly,good performances were observed(rRMSE=0:11)when the model is trained over synthetic low-resolution images obtained by downsampling the native training high-resolution images and applied to the synthetic low-resolution validation images.Conversely,poor performances are obtained when the model is trained on a given spatial resolution and applied to another spatial resolution.Training on a mix of high-and low-resolution images allows to get very good performances on the native high-resolution(rRMSE=0:06)and synthetic low-resolution(rRMSE=0:10)images.However,very low performances are still observed over the native low-resolution images(rRMSE=0:48),mainly due to the poor quality of the native low-resolution images.Finally,an advanced super resolution method based on GAN(generative adversarial network)that introduces additional textural information derived from the native high-resolution images was applied to the native low-resolution validation images.Results show some significant improvement(rRMSE=0:22)compared to bicubic upsampling approach,while still far below the performances achieved over the native high-resolution images.
文摘Multispectral observations from unmanned aerial vehicles(UAVs)are currently used for precision agriculture and crop phenotyping applications to monitor a series of traits allowing the characterization of the vegetation status.However,the limited autonomy of UAVs makes the completion of flights difficult when sampling large areas.Increasing the throughput of data acquisition while not degrading the ground sample distance(GSD)is,therefore,a critical issue to be solved.We propose here a new image acquisition configuration based on the combination of two focal length(f)optics:an optics with f=4:2 mm is added to the standard f=8 mm(SS:single swath)of the multispectral camera(DS:double swath,double of the standard one).Two flights were completed consecutively in 2018 over a maize field using the AIRPHEN multispectral camera at 52 m altitude.The DS flight plan was designed to get 80%overlap with the 4.2 mm optics,while the SS one was designed to get 80%overlap with the 8 mm optics.As a result,the time required to cover the same area is halved for the DS as compared to the SS.The georeferencing accuracy was improved for the DS configuration,particularly for the Z dimension due to the larger view angles available with the small focal length optics.Application to plant height estimates demonstrates that the DS configuration provides similar results as the SS one.However,for both the DS and SS configurations,degrading the quality level used to generate the 3D point cloud significantly decreases the plant height estimates.
基金supported by several projects including ANR PHENOME(Programme d’investissement d’avenir ANR11INBS0012)#Digitag(PIA Institut Convergences Agriculture Numérique ANR16CONV0004)CASDAR LITERAL.
文摘The sowing pattern has an important impact on light interception efficiency in maize by determining the spatial distribution of leaves within the canopy.Leaves orientation is an important architectural trait determining maize canopies light interception.Previous studies have indicated how maize genotypes may adapt leaves orientation to avoid mutual shading with neighboring plants as a plastic response to intraspecific competition.The goal of the present study is 2-fold:firstly,to propose and validate an automatic algorithm(Automatic Leaf Azimuth Estimation from Midrib detection[ALAEM])based on leaves midrib detection in vertical red green blue(RGB)images to describe leaves orientation at the canopy level;and secondly,to describe genotypic and environmental differences in leaves orientation in a panel of 5 maize hybrids sowing at 2 densities(6 and 12 plants.m^(−2))and 2 row spacing(0.4 and 0.8 m)over 2 different sites in southern France.The ALAEM algorithm was validated against in situ annotations of leaves orientation,showing a satisfactory agreement(root mean square[RMSE]error=0.1,R^(2)=0.35)in the proportion of leaves oriented perpendicular to rows direction across sowing patterns,genotypes,and sites.The results from ALAEM permitted to identify significant differences in leaves orientation associated to leaves intraspecific competition.In both experiments,a progressive increase in the proportion of leaves oriented perpendicular to the row is observed when the rectangularity of the sowing pattern increases from 1(6 plants.m^(−2),0.4 m row spacing)towards 8(12 plants.m^(−2),0.8 m row spacing).Significant differences among the 5 cultivars were found,with 2 hybrids exhibiting,systematically,a more plastic behavior with a significantly higher proportion of leaves oriented perpendicularly to avoid overlapping with neighbor plants at high rectangularity.Differences in leaves orientation were also found between experiments in a squared sowing pattern(6 plants.m^(−2),0.4 m row spacing),indicating a possible contribution of illumination conditions inducing a preferential orientation toward east-west direction when intraspecific competition is low.
基金supported by the National Key R&D Program of China(nos.2021YFD2000105 and 2022YFE0116200)Jiangsu Funding Program for Excellent Postdoctoral Talent(no.2022ZB349)+3 种基金Young Scientists Fund of the Natural Science Foundation of Jiangsu Province,China(no.BK20210411)Young Scientists Fund of the National Natural Science Foundation of China(no.42201437)Fundamental Research Funds for the Central Universities of Ministry of Education of China(no.KYCXJC2022005)Project of Seed Industry Revitalization in Jiangsu Province,China(JBGS[2021]007).
文摘The number of leaves at a given time is important to characterize plant growth and development.In this work,we developed a high-throughput method to count the number of leaves by detecting leaf tips in RGB images.The digital plant phenotyping platform was used to simulate a large and diverse dataset of RGB images and corresponding leaf tip labels of wheat plants at seedling stages(150,000 images with over 2 million labels).The realism of the images was then improved using domain adaptation methods before training deep learning models.The results demonstrate the efficiency of the proposed method evaluated on a diverse test dataset,collecting measurements from 5 countries obtained under different environments,growth stages,and lighting conditions with different cameras(450 images with over 2,162 labels).Among the 6 combinations of deep learning models and domain adaptation techniques,the Faster-RCNN model with cycle-consistent generative adversarial network adaptation technique provided the best performance(R^(2)=0.94,root mean square error=8.7).Complementary studies show that it is essential to simulate images with sufficient realism(background,leaf texture,and lighting conditions)before applying domain adaptation techniques.Furthermore,the spatial resolution should be better than 0.6 mm per pixel to identify leaf tips.The method is claimed to be self-supervised since no manual labeling is required for model training.The self-supervised phenotyping approach developed here offers great potential for addressing a wide range of plant phenotyping problems.The trained networks are available at https://github.com/YinglunLi/Wheat-leaf-tip-detection.
基金supported by the National Natural Science Foundation of China[grant number 41901298]the Open Fund of State Key Laboratory of Remote Sensing Science[grant number OFSLRSS201924]+1 种基金the Open Research Fund of Key Laboratory of Digital Earth Science,Institute of Remote Sensing and Digital Earth,Chinese Academy of Sciences[grant number 2018LDE002]the Fundamental Research Funds for the Central Universities[grant number 2652018031].
文摘The MODIS LAI/FPAR products have been widely used in various fields since their first public release in 2000.This review intends to summarize the history,development trends,scientific collaborations,disciplines involved,and research hotspots of these products.Its aim is to intrigue researchers and stimulate new research direction.Based on literature data from the Web of Science(WOS)and associated funding information,we conducted a bibliometric visualization review of the MODIS LAI/FPAR products from 1995 to 2020 using bibliometric and social network analysis(SNA)methods.We drew the following conclusions:(1)research based on the MODIS LAI/FPAR shows an upward trend with a multiyear average growth rate of 24.9%in the number of publications.(2)Researchers from China and the USA are the backbone of this research area,among which the Chinese Academy of Sciences(CAS)is the core research institution.(3)Research based on the MODIS LAI/FPAR covers a wide range of disciplines but mainly focus on environmental science and ecology.(4)Ecology,crop production estimation,algorithm improvement,and validation are the hotspots of these studies.(5)Broadening the research field,improving the algorithms,and overcoming existing difficulties in heterogeneous surface,scale effects,and complex terrains will be the trend of future research.Our work provides a clear view of the development of the MODIS LAI/FPAR products and valuable information for scholars to broaden their research fields.
基金the Strategic Priority Research Program of the Chinese Academy of Sciences(Grant No.XDA24020202)Plant Phenomics Research Program of Science and Technology Department of Jiangsu Province(No.BM2018001)Beijing Municipal Science and Technology Project(Z191100007419004).
文摘Plant growth rhythm in structural traits is important for better understanding plant response to the ever-changing environment.Terrestrial laser scanning(TLS)is a well-suited tool to study structural rhythm under field conditions.Recent studies have used TLS to describe the structural rhythm of trees,but no consistent patterns have been drawn.Meanwhile,whether TLS can capture structural rhythm in crops is unclear.Here,we aim to explore the seasonal and circadian rhythms in maize structural traits at both the plant and leaf levels from time-series TLS.The seasonal rhythm was studied using TLS data collected at four key growth periods,including jointing,bell-mouthed,heading,and maturity periods.Circadian rhythms were explored by using TLS data acquired around every 2 hours in a whole day under standard and cold stress conditions.Results showed that TLS can quantify the seasonal and circadian rhythm in structural traits at both plant and leaf levels.(1)Leaf inclination angle decreased significantly between the jointing stage and bell-mouthed stage.Leaf azimuth was stable after the jointing stage.(2)Some individual-level structural rhythms(e.g.,azimuth and projected leaf area/PLA)were consistent with leaf-level structural rhythms.(3)The circadian rhythms of some traits(e.g.,PLA)were not consistent under standard and cold stress conditions.(4)Environmental factors showed better correlations with leaf traits under cold stress than standard conditions.Temperature was the most important factor that significantly correlated with all leaf traits except leaf azimuth.This study highlights the potential of time-series TLS in studying outdoor agricultural chronobiology.
基金The French team received support from ANRT for the CIFRE grant of Etienne David,cofunded by Arvalis.The study was partly supported by several projects including ANR PHENOME,ANR BREEDWHEAT,CASDAR LITERAL,and FSOV“Plastix”.Many thanks are due to the people who annotated the French datasets,including Frederic Venault,Xiuliang Jin,Mario Serouard,Ilias Sarbout,Carole Gigot,Eloïse Issert,and Elise Lepage.The Japanese team received support from JST CREST(Grant Numbers JPMJCR16O3,JPMJCR16O2,and JPMJCR1512)and MAFF Smart-Breeding System for Innovative Agriculture(BAC1003),Japan.Many thanks are due to the people who annotated the Japanese dataset,including Kozue Wada,Masanori Ishii,Ryuuichi Kanzaki,Sayoko Ishibashi,and Sumiko Kaneko.The Canadian team received funding from the Plant Phenotyping and Imaging Research Center through a grant from the Canada First Research Excellence Fund.Many thanks are due to Steve Shirtliffe,Scott Noble,Tyrone Keep,Keith Halco,and Craig Gavelin for managing the field site and collecting images.Rothamsted Research received support from the Biotechnology and Biological Sciences Research Council(BBSRC)of the United Kingdom as part of the Designing Future Wheat(BB/P016855/1)project.We are also thankful to Prof.MalcolmJ.Hawkesford,who leads the DFWproject and Dr.Nicolas Virlet for conducting the experiment at Rothamsted Research.The Gatton,Australia dataset was collected on a field trial conducted by CSIRO and UQ,with trial conduct and measurements partly funded by the Grains Research and Development Corporation(GRDC)in project CSP00179.A new GRDC project involves several of the authors and supports their contribution to this paper.The dataset collected in China was supported by the Program for High-Level Talents Introduction of Nanjing Agricultural University(440—804005).Many thanks are due to Jie Zhou and many volunteers from Nanjing Agricultural University to accomplish the annotation.The dataset collection at ETHZ was supported by Prof.AchimWalter,who leads the Crop Science group.Many thanks are due to Kevin Keller for the initial preparation of the ETHZ dataset and Lara Wyser,Ramon Winterberg,Damian Käch,Marius Hodel,and Mario Serouard(INRAE)for the annotation of the ETHZ dataset and to Brigita Herzog and Hansueli Zellweger for crop husbandry.
文摘The detection of wheat heads in plant images is an important task for estimating pertinent wheat traits including head population density and head characteristics such as health,size,maturity stage,and the presence of awns.Several studies have developed methods for wheat head detection from high-resolution RGB imagery based on machine learning algorithms.However,these methods have generally been calibrated and validated on limited datasets.High variability in observational conditions,genotypic differences,development stages,and head orientation makes wheat head detection a challenge for computer vision.Further,possible blurring due to motion or wind and overlap between heads for dense populations make this task even more complex.Through a joint international collaborative effort,we have built a large,diverse,and well-labelled dataset of wheat images,called the Global Wheat Head Detection(GWHD)dataset.It contains 4700 high-resolution RGB images and 190000 labelled wheat heads collected from several countries around the world at different growth stages with a wide range of genotypes.Guidelines for image acquisition,associating minimum metadata to respect FAIR principles,and consistent head labelling methods are proposed when developing new head detection datasets.The GWHD dataset is publicly available at http://www.global-wheat.com/and aimed at developing and benchmarking methods for wheat head detection.
基金support from ANRT for the CIFRE grant of E.D.,cofunded by Arvalispartly supported by several projects,including:Canada:The Canada First Research Excellence Fund and the Global Institute Food Security,University of Saskatchewan supported the organization of the competition.+2 种基金rance:PIA#Digitag Institut Convergences Agriculture Numérique,Hiphen sup-ported the organization of the competition and the Agence Nationale de la Recherche projects ANR-11-INBS-0012(Phenome)Japan:Kubota supported the organization of the competitionAustralia:Grains Research and Development Corporation(UOQ2002-008RTX Machine learning applied to high-throughput feature extraction from imagery to map spatial variability and UOQ2003-011RTX INVITA-A technol-ogy and analytics platform for improving variety selection)supported competition and data provision/discussions.
文摘Data competitions have become a popular approach to crowdsource new data analysis methods for general and specialized data science problems.Data competitions have a rich history in plant phenotyping,and new outdoor field datasets have the potential to embrace solutions across research and commercial applications.We developed the Global Wheat Challenge as a generalization competition in 2020 and 2021 to find more robust solutions for wheat head detection using field images from different regions.We analyze the winning challenge solutions in terms of their robustness when applied to new datasets.We found that the design of the competition had an influence on the selection of winning solutions and provide recommendations for future competitions to encourage the selection of more robust solutions.
基金the Grand Est region for funding the purchase of the high-throughput phenotyping system and the Ph.D.thesis of E.C.the“Plant Biology and Breeding”INRAE department for its fnancial support.
文摘The strong societal demand to reduce pesticide use and adaptation to climate change challenges the capacities of phenotyping new varieties in the vineyard.High-throughput phenotyping is a way to obtain meaningful and reliable information on hundreds of genotypes in a limited period.We evaluated traits related to growth in 209 genotypes from an interspecific grapevine biparental cross,between IJ119,a local genitor,and Divona,both in summer and in winter,using several methods:fresh pruning wood weight,exposed leaf area calculated from digital images,leaf chlorophyll concentration,and LiDAR-derived apparent volumes.Using high-density genetic information obtained by the genotyping by sequencing technology(GBS),we detected 6 regions of the grapevine genome[quantitative trait loci(QTL)]associated with the variations of the traits in the progeny.The detection of statistically significant QTLs,as well as correlations(R^(2))with traditional methods above 0.46,shows that LiDAR technology is effective in characterizing the growth features of the grapevine.Heritabilities calculated with LiDAR-derived total canopy and pruning wood volumes were high,above 0.66,and stable between growing seasons.These variables provided genetic models explaining up to 47%of the phenotypic variance,which were better than models obtained with the exposed leaf area estimated from images and the destructive pruning weight measurements.Our results highlight the relevance of LiDAR-derived traits for characterizing genetically induced differences in grapevine growth and open new perspectives for high-throughput phenotyping of grapevines in the vineyard.
