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High-throughput multiplexed fluorescence-activated droplet sorting

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Microsystems & Nanoengineering 2018年第1期4卷 95-104页

作者： Ouriel Caen Simon Schütz M.S.Suryateja Jammalamadaka Jérémy Vrignon Philippe Nizard Tobias M.Schneider jean-christophe baret Valérie Taly INSERM UMR-S1147 CNRS SNC5014Paris Descartes UniversityEquipe labellisée Ligue Nationale contre le cancerParisFrance Emergent Complexity in Physical Systems Laboratory(ECPS) Ecole Polytechnique Fédérale de Lausanne1015 LausanneSwitzerland CNRS University BordeauxCRPPUMR 5031115 Avenue Schweitzer33600 PessacFrance

Fluorescence-activated droplet sorting(FADS)is one of the most important features provided by droplet-based ***,to date,it does not allow to compete with the high-throughput multiplexed sorting capabilities offered by flow ***,we demonstrate the use of a dielectrophoretic-based FADS,allowing to sort up to five different droplet populations *** system provides means to select droplets of different phenotypes in a single experimental run to separate initially heterogeneous *** experimental results are rationalized with the help of a numerical model of the actuation of droplets in electric fields providing guidelines for the prediction of sorting designs for upscaled or downscaled microsystems.

关键词： rational initially sorting

Global Wheat Head Detection Challenges:Winning Models and Application for Head Counting

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Plant Phenomics 2023年第3期5卷 460-473页

作者： Etienne David Franklin Ogidi Daniel Smith Scott Chapman Benoit de Solan Wei Guo Frederic baret Ian Stavness UMR 1114 EMMAH INRAEAvignonFrance Arvalis–Institut du Végétal ParisFrance Department of Computer Science University of SaskatchewanSaskatoonCanada School of Food and Agricultural Sciences University of QueenslandBrisbaneAustralia Graduate School of Agricultural and Life Sciences The University of TokyoTokyoJapan

Data competitions have become a popular approach to crowdsource new data analysis methods for general and specialized data science *** competitions have a rich history in plant phenotyping,and new outdoor field datasets have the potential to embrace solutions across research and commercial *** 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 *** analyze the winning challenge solutions in terms of their robustness when applied to new *** 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.

关键词： Wheat specialized competition

Plant Phenomics:Emerging Transdisciplinary Science

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Plant Phenomics 2019年第1期1卷 39-41页

作者： Seishi Ninomiya Frédéric baret Zong-Ming(Max)Cheng University of Tokyo TokyoJapan French National Institute of Agricultural Research ParisFrance Nanjing Agricultural University NanjingChina University of Tennessee TNUSA

Humankind is facing an unprecedented challenge to produce enough food for the coming decades because of population growth and increase in the average demand per capita,changes in climate conditions,and limitations in arable land area,as well as pressure on the water and *** main avenues should be concurrently taken to increase crop productivity:improving genetics to get more efficient and resilient crops and developing optimal crop management *** description and understanding of crop functioning will therefore be instrumental both for genetic improvement and crop *** will help to associate functional traits with the genome which will accelerate genetic progress by having more efficient techniques to design ideotypes adapted to particular pedoclimatic and crop management conditions and create them from the available genetic diversity.

关键词： crops Plant adapted

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带主动磁轴承单盘转子的坠落瞬态响应

上海交通大学学报 1996年第7期30卷 5-11页

作者：方之楚 L.E.baret 上海交通大学工程力学系美国弗吉尼亚大学机械工程系

就磁轴承发生故障时转子坠入备用轴承所产生的非线性瞬态响应，提出了摩擦力的计算模型，从理论上建立了更为精确的运动微分方程．数值积分结果预示，当备用轴承具有很小阻尼或很大阻尼时，会导致系统破坏的逆回旋．为保证系统安全运行。

关键词：转子系统磁轴承备用轴承摩擦力瞬态响应

From Prototype to Inference:A Pipeline to Apply Deep Learning in Sorghum Panicle Detection

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Plant Phenomics 2023年第1期5卷 94-109页

作者： Chrisbin James Yanyang Gu Andries Potgieter Etienne David Simon Madec Wei Guo Frédéric baret Anders Eriksson Scott Chapman School of Agriculture and Food Sciences The University of QueenslandBrisbaneAustralia School of Information Technology and Electrical Engineering The University of QueenslandBrisbaneAustralia Queensland Alliance for Agriculture and Food Innovation The University of QueenslandBrisbaneAustralia Arvalis Institut du VégétalParisFrance Graduate School of Agricultural and Life Sciences The University of TokyoTokyoJapan Institut National de la Recherche Agronomique ParisFrance

Head(panicle)density is a major component in understanding crop yield,especially in crops that produce variable numbers of tillers such as sorghum and *** of panicle density both in plant breeding and in the agronomy scouting of commercial crops typically relies on manual counts observation,which is an inefficient and tedious *** of the easy availability of red–green–blue images,machine learning approaches have been applied to replacing manual ***,much of this research focuses on detection per se in limited testing conditions and does not provide a general protocol to utilize deep-learning-based *** this paper,we provide a comprehensive pipeline from data collection to model deployment in deep-learning-assisted panicle yield estimation for *** pipeline provides a basis from data collection and model training,to model validation and model deployment in commercial *** model training is the foundation of the ***,in natural environments,the deployment dataset is frequently different from the training data(domain shift)causing the model to fail,so a robust model is essential to build a reliable *** we demonstrate our pipeline in a sorghum field,the pipeline can be generalized to other grain *** pipeline provides a high-resolution head density map that can be utilized for diagnosis of agronomic variability within a field,in a pipeline built without commercial software.

关键词： crops breeding utilize

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 *** orientation is an important architectural trait determining maize canopies light *** studies have indicated how maize genotypes may adapt leaves orientation to avoid mutual shading with neighboring plants as a plastic response to intraspecific *** 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 *** 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 *** results from ALAEM permitted to identify significant differences in leaves orientation associated to leaves intraspecific *** 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 *** in leaves orientation were also found between experiments in a squared sowing pattern(6 plants.m^(−2),0.4 m

关键词： cultivar spacing pattern

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 *** this work,we developed a high-throughput method to count the number of leaves by detecting leaf tips in RGB *** 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 *** 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 ***,the spatial resolution should be better than 0.6 mm per pixel to identify leaf *** method is claimed to be self-supervised since no manual labeling is required for model *** self-supervised phenotyping approach developed here offers great potential for addressing a wide range of plant phenotyping *** trained networks are available at https://***/YinglunLi/Wheat-leaf-tip-detection.

关键词： Wheat seedling Plant

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 *** use of RGB images taken from UAVs may replace the traditional visual counting in fields with improved throughput,accuracy,and access to plant ***,high-resolution images are required to detect the small plants present at the early *** 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 *** collected at high resolution(GSD≈0:3 cm)over six contrasted sites were used for model *** additional sites with images acquired both at high and low(GSD≈0:6 cm)resolutions were used to evaluate the model *** 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 ***,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 ***,poor performances are obtained when the model is trained on a given spatial resolution and applied to another spatial *** 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)***,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 ***,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 *** show some signi

关键词： RCNN Faster Image

High spatial resolution imaging solutions for highthroughput field phenotyping

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High spatial resolution imaging solutions for highthroughput...