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Transient response of a concrete tunnel in an elastic rock with imperfect contact

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International Journal of Mining Science and Technology 2020年第5期30卷 605-612页

作者： R.Shakeri A.Mesgouez G.Lefeuve-Mesgouez umr 1114 emmah Avignon UniversityF-84916 AvignonFrance Department of Civil Environmental&Architectural EngineeringUniversity of Colorado BoulderCO 80309USA

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.

关键词： Dynamic response Imperfect interface Circular tunnel Semi-analytical approach Transient wave propagation

LiDAR Is Effective in Characterizing Vine Growth and Detecting Associated Genetic Loci

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Plant Phenomics 2023年第4期5卷 836-851页

作者： Elsa Chedid Komlan Avia Vincent Dumas Lionel Ley Nicolas Reibel Gisèle Butterlin Maxime Soma Raul Lopez-Lozano Frédéric Baret Didier Merdinoglu Éric Duchêne INRAE University of StrasbourgUMR SVQV28rue de Herrlisheim68000 ColmarFrance INRAE UEAV28rue de Herrlisheim68000 ColmarFrance INRAE Aix-Marseille UniversitéUMR RECOVER3275 Route de Cézanne13182 Aix-en-ProvenceFrance INRAE Avignon UniversitéUMR EMMAHUMT CAPTE228route de l'aérodrome84914 AvignonFrance

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.

关键词： LiDAR traits hundreds

Analyzing Changes in Maize Leaves Orientation due to GxExM Using an Automatic Method from RGB Images

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Plant Phenomics 2023年第2期5卷 239-252页

作者： Mario Serouart Raul Lopez-Lozano Gaëtan Daubige Maëva Baumont Brigitte Escale Benoit De Solan Frédéric Baret Arvalis Institut du végétal228route de l’aérodrome-CS 4050984914 Avignon Cedex 9France INRAE Avignon UniversitéUMR EMMAHUMT CAPTE228route de l’aérodrome-CS 4050984914 Avignon Cedex 9France Arvalis Ecophysiology21 Chemin de Pau64121 MontardonFrance

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

关键词： cultivar spacing pattern

Global sensitivity analysis for choosing the main soil parameters of a crop model to be determined

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Agricultural Sciences 2012年第7期3卷 949-961页

作者： Hubert Varella Samuel Buis Marie Launay Martine Guérif INRA UMR 1114 INRA-UAPV EMMAH Avignon France INRA Unite Agroclim Avignon France Syngenta Crop Protection Münchwilen AG Stein/AG Switzerland

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.

关键词： Global Sensitivity Analysis Uncertainty Analysis Soil Parameters Crop Model STICS Management Decision Support Agro-Environmental Variables

Scoring Cercospora Leaf Spot on Sugar Beet:Comparison of UGV and UAV Phenotyping Systems

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Plant Phenomics 2020年第1期2卷 225-242页

作者： S.Jay A.Comar R.Benicio J.Beauvois D.Dutartre G.Daubige W.Li J.Labrosse S.Thomas N.Henry M.Weiss F.Baret INRAE UMR 114 EMMAHUMT CAPTEF-84914 AvignonFrance HIPHEN SAS 84000 AvignonFrance ARVALIS-Institut du végétal 84000 AvignonFrance Florimond Desprez 59242 Capelle-en-PévèleFrance

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.

关键词： illumination millimeter critical

A Bibliometric Visualization Review of the MODIS LAI/FPAR Products from 1995 to 2020

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Journal of Remote Sensing 2021年第1期2021卷 74-93页

作者： Kai Yan Dongxiao Zou Guangjian Yan Hongliang Fang Marie Weiss Miina Rautiainen Yuri Knyazikhin Ranga B.Myneni School of Land Science and Techniques China University of GeosciencesChina State Key Laboratory of Remote Sensing Science Jointly Sponsored by Chinese Academy of Sciences and Beijing Normal UniversityChina Department of Earth and Environment Boston UniversityUSA LREIS Institute of Geographic Sciences and Natural Resources ResearchChinese Academy of SciencesChina INRAE Avignon UniversitéUMR 1114 EMMAHUMT CAPTEF-84000 AvignonFrance Department of Built Environment School of EngineeringAalto UniversityFinland Department of Electronics and Nanoengineering School of Electrical EngineeringAalto UniversityFinland

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.

关键词： FPAR backbone estimation

Self-Supervised Plant Phenotyping by Combining Domain Adaptation with 3D Plant Model Simulations: Application to Wheat Leaf Counting at Seedling Stage

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Plant Phenomics 2023年第2期5卷 226-238页

作者： Yinglun Li Xiaohai Zhan Shouyang Liu Hao Lu Ruibo Jiang Wei Guo Scott Chapman Yufeng Ge Benoit de Solan Yanfeng Ding Frédéric Baret Plant Phenomics Research Centre Academy for Advanced Interdisciplinary StudiesJiangsu Collaborative Innovation Center for Modern Crop ProductionNanjing Agricultural UniversityNanjingChina Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and AutomationHuazhong University of Science and TechnologyWuhanChina Graduate School of Agricultural and Life Sciences The University of Tokyo1-1-1 Midori-choNishitokyo CityTokyoJapan School of Agriculture and Food Sciences The University of QueenslandSt.LuciaQueensland 4072Australia Department of Biological Systems Engineering University of Nebraska-LincolnLincolnNebraska 68583United States INRAE Avignon UniversitéUMR EMMAHUMT CAPTE228route de l’aérodrome-CS 4050984914 Avignon Cedex 9France ARVALIS Institut du Végétal 3 Rue Joseph et Marie Hackin75116 ParisFrance

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://***/YinglunLi/Wheat-leaf-tip-detection.

关键词： Wheat seedling Plant

A Double Swath Configuration for Improving Throughput and Accuracy of Trait Estimate from UAV Images

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Plant Phenomics 2021年第1期3卷 378-388页

作者： Wenjuan Li Alexis Comar Marie Weiss Sylvain Jay Gallian Colombeau Raul Lopez-Lozano Simon Madec Frédéric Baret HIPHEN 228 Route de l’Aérodrome84000 AvignonFrance INRAE Avignon UniversitéUMR EMMAH84000 AvignonFrance ARVALIS Institut du Végétal 3 Rue Joseph et Marie Hackin75116 ParisFrance

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.

关键词： optics overlap altitude

Estimates of Maize Plant Density from UAV RGB Images Using Faster-RCNN Detection Model:Impact of the Spatial Resolution

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Plant Phenomics 2021年第1期3卷 181-196页

作者： K.Velumani R.Lopez-Lozano S.Madec W.Guo J.Gillet A.Comar F.Baret Hiphen SAS 120 Rue Jean DaussetAgroparcBâtiment Technicité84140 AvignonFrance INRAE UMR EMMAHUMT CAPTE228 Route de l'AérodromeDomaine Saint Paul-Site Agroparc CS 4050984914 Avignon Cedex 9France Arvalis 228Route de l'Aérodrome-CS 4050984914 Avignon Cedex 9France International Field Phenomics Research Laboratory Institute for Sustainable Agro-Ecosystem ServicesGraduate School of Agricultural and Life SciencesThe University of TokyoTokyoJapan

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 bicubi

关键词： RCNN Faster Image