Plant phenomics and high-throughput phenotyping: accelerating rice functional genomics using multidisciplinary technologies.

[1]  Anna N. Stepanova,et al.  Plant Functional Genomics , 2015, Methods in Molecular Biology.

[2]  Fabio Fiorani,et al.  The art of growing plants for experimental purposes: a practical guide for the plant biologist. , 2012, Functional plant biology : FPB.

[3]  Hendrik Poorter,et al.  Pot size matters: a meta-analysis of the effects of rooting volume on plant growth. , 2012, Functional plant biology : FPB.

[4]  Wanneng Yang,et al.  Acceleration of CT reconstruction for wheat tiller inspection based on adaptive minimum enclosing rectangle , 2012 .

[5]  Christian Klukas,et al.  Analysis of high-throughput plant image data with the information system IAP , 2012, J. Integr. Bioinform..

[6]  J. Fripp,et al.  A novel mesh processing based technique for 3D plant analysis , 2012, BMC Plant Biology.

[7]  Thomas Neuberger,et al.  Surveying the plant's world by magnetic resonance imaging. , 2012, The Plant journal : for cell and molecular biology.

[8]  Yong He,et al.  Early detection of rice blast ( Pyricularia ) at seedling stage in Nipponbare rice variety using near-infrared hyper-spectral image , 2012 .

[9]  Lihong V. Wang,et al.  Photoacoustic Tomography: In Vivo Imaging from Organelles to Organs , 2012, Science.

[10]  Xiang-Dong Liu,et al.  Hyperspectral detection of rice damaged by rice leaf folder (Cnaphalocrocis medinalis) , 2012 .

[11]  Masayuki Hirafuji,et al.  Field monitoring support system for the occurrence of Leptocorisa chinensis Dallas (Hemiptera: Alydidae) using synthetic attractants, Field Servers, and image analysis , 2012 .

[12]  S. Phadikar,et al.  Classification of Rice Leaf Diseases Based onMorphological Changes , 2012 .

[13]  Søren B. Hansen,et al.  The use of PET/CT scanning technique for 3D visualization and quantification of real-time soil/plant interactions , 2011, Plant and Soil.

[14]  Lingfeng Duan,et al.  A novel machine-vision-based facility for the automatic evaluation of yield-related traits in rice , 2011, Plant Methods.

[15]  Lijun Luo,et al.  Natural variation in GS5 plays an important role in regulating grain size and yield in rice , 2011, Nature Genetics.

[16]  M. Tester,et al.  Phenomics--technologies to relieve the phenotyping bottleneck. , 2011, Trends in plant science.

[17]  T. Sakamoto,et al.  Assessment of digital camera-derived vegetation indices in quantitative monitoring of seasonal rice growth , 2011 .

[18]  L. Plümer,et al.  Robust fitting of fluorescence spectra for pre-symptomatic wheat leaf rust detection with Support Vector Machines , 2011 .

[19]  Philip N Benfey,et al.  From lab to field, new approaches to phenotyping root system architecture. , 2011, Current opinion in plant biology.

[20]  M. Tester,et al.  Genetic analysis of abiotic stress tolerance in crops. , 2011, Current opinion in plant biology.

[21]  R. MacCurdy,et al.  Three-Dimensional Root Phenotyping with a Novel Imaging and Software Platform1[C][W][OA] , 2011, Plant Physiology.

[22]  Werner B. Herppich,et al.  Hyperspectral and Chlorophyll Fluorescence Imaging to Analyse the Impact of Fusarium culmorum on the Photosynthetic Integrity of Infected Wheat Ears , 2011, Sensors.

[23]  Q. Luo,et al.  High-throughput measurement of rice tillers using a conveyor equipped with x-ray computed tomography. , 2011, The Review of scientific instruments.

[24]  Eiji Takada,et al.  Estimating Paddy Rice Leaf Area Index with Fixed Point Continuous Observation of Near Infrared Reflectance Using a Calibrated Digital Camera , 2011 .

[25]  Eiji Takada,et al.  Regression-Based Models to Predict Rice Leaf Area Index Using Biennial Fixed Point Continuous Observations of Near Infrared Digital Images , 2011 .

[26]  Qingming Luo,et al.  Original paper: Fast discrimination and counting of filled/unfilled rice spikelets based on bi-modal imaging , 2011 .

[27]  Zhiyan Zhou,et al.  Color-Based Corner Detection Algorithm for Rice Plant-hopper Infestation Area on Rice Stem Using the RGB Color Space , 2011 .

[28]  Jayamala K. Patil,et al.  Advances in Image Processing for Detection of Plant Disease , 2017 .

[29]  Yong He,et al.  Identification of Broken Rice Kernels Using Image Analysis Techniques Combined with Velocity Representation Method , 2010, Food and Bioprocess Technology.

[30]  Falk Schreiber,et al.  HTPheno: An image analysis pipeline for high-throughput plant phenotyping , 2011, BMC Bioinformatics.

[31]  Ross A Frick,et al.  Accurate inference of shoot biomass from high-throughput images of cereal plants , 2011, Plant Methods.

[32]  Noel D.G. White,et al.  Identification of insect-damaged wheat kernels using short-wave near-infrared hyperspectral and digital colour imaging , 2010 .

[33]  M. Tester,et al.  High-throughput shoot imaging to study drought responses. , 2010, Journal of experimental botany.

[34]  H. Jones,et al.  New phenotyping methods for screening wheat and barley for beneficial responses to water deficit. , 2010, Journal of experimental botany.

[35]  Malte Helmert,et al.  The Scanalyzer Domain: Greenhouse Logistics as a Planning Problem , 2010, ICAPS.

[36]  Qifa Zhang,et al.  Genetic and molecular bases of rice yield. , 2010, Annual review of plant biology.

[37]  B. Andrieu,et al.  Functional-structural plant modelling: a new versatile tool in crop science. , 2010, Journal of experimental botany.

[38]  F. Tardieu,et al.  Dissection and modelling of abiotic stress tolerance in plants. , 2010, Current opinion in plant biology.

[39]  M. Ikeda,et al.  Analysis of rice panicle traits and detection of QTLs using an image analyzing method , 2010 .

[40]  P. Langridge,et al.  Breeding Technologies to Increase Crop Production in a Changing World , 2010, Science.

[41]  P. Benfey,et al.  Imaging and Analysis Platform for Automatic Phenotyping and Trait Ranking of Plant Root Systems , 2010 .

[42]  Jingfeng Huang,et al.  Discrimination of rice panicles by hyperspectral reflectance data based on principal component analysis and support vector classification , 2010, Journal of Zhejiang University SCIENCE B.

[43]  Raju Naik Vankadavath,et al.  Computer aided data acquisition tool for high-throughput phenotyping of plant populations , 2009, Plant Methods.

[44]  Peng Lin,et al.  A prediction model for population occurrence of paddy stem borer (Scirpophaga incertulas), based on Back Propagation Artificial Neural Network and Principal Components Analysis , 2009 .

[45]  Josse De Baerdemaeker,et al.  Hyperspectral waveband selection for on-line measurement of grain cleanness , 2009 .

[46]  Ulrich Schurr,et al.  Combined MRI-PET dissects dynamic changes in plant structures and functions. , 2009, The Plant journal : for cell and molecular biology.

[47]  E. Finkel Imaging. With 'phenomics,' plant scientists hope to shift breeding into overdrive. , 2009, Science.

[48]  C. Joerdens,et al.  Evaluation of leaf water status by means of permittivity at terahertz frequencies , 2009, Journal of biological physics.

[49]  Shahab Sokhansanj,et al.  Sieveless particle size distribution analysis of particulate materials through computer vision , 2009 .

[50]  Gianfranco Venora,et al.  Quality assessment of durum wheat storage centres in Sicily: Evaluation of vitreous, starchy and shrunken kernels using an image analysis system , 2009 .

[51]  M. Tester,et al.  Quantifying the three main components of salinity tolerance in cereals. , 2009, Plant, cell & environment.

[52]  J. Micol Leaf development: time to turn over a new leaf? , 2009, Current opinion in plant biology.

[53]  Eiji Takada,et al.  Continuous Monitoring of Visible and Near-Infrared Band Reflectance from a Rice Paddy for Determining Nitrogen Uptake Using Digital Cameras , 2009 .

[54]  C. Igathinathane,et al.  Major orthogonal dimensions measurement of food grains by machine vision using ImageJ , 2009 .

[55]  Samsuzana Abd Aziz,et al.  Quantifying sub-pixel signature of paddy rice field using an artificial neural network , 2009 .

[56]  R. Furbank,et al.  Plant phenomics: from gene to form and function , 2009 .

[57]  P. Govindaraj,et al.  Analysing genetic control of cooked grain traits and gelatinization temperature in a double haploid population of rice by quantitative trait loci mapping , 2009, Euphytica.

[58]  Qifa Zhang,et al.  RID1, encoding a Cys2/His2-type zinc finger transcription factor, acts as a master switch from vegetative to floral development in rice , 2008, Proceedings of the National Academy of Sciences.

[59]  Xing Wang Deng,et al.  Rice 2020: a call for an international coordinated effort in rice functional genomics. , 2008, Molecular plant.

[60]  Lei Wang,et al.  A triallelic system of S5 is a major regulator of the reproductive barrier and compatibility of indica–japonica hybrids in rice , 2008, Proceedings of the National Academy of Sciences.

[61]  J. Trygg,et al.  LAMINA: a tool for rapid quantification of leaf size and shape parameters , 2008, BMC Plant Biology.

[62]  N. Baker Chlorophyll fluorescence: a probe of photosynthesis in vivo. , 2008, Annual review of plant biology.

[63]  Xu Wang,et al.  DETERMINATION OF THE PROTEIN CONTENT IN RICE BY THE DIGITAL CHROMATIC METHOD , 2008 .

[64]  Noel D.G. White,et al.  Thermal imaging to detect infestation by Cryptolestes ferrugineus inside wheat kernels , 2008 .

[65]  Qifa Zhang Strategies for developing Green Super Rice , 2007, Proceedings of the National Academy of Sciences.

[66]  S. Ninomiya,et al.  Chalkiness in Rice: Potential for Evaluation with Image Analysis , 2007 .

[67]  Digvir S. Jayas,et al.  Dual energy X-ray image analysis for classifying vitreousness in durum wheat , 2007 .

[68]  Noel D.G. White,et al.  Detection of sprouted wheat kernels using soft X-ray image analysis , 2007 .

[69]  Oliver Tackenberg,et al.  A new method for non-destructive measurement of biomass, growth rates, vertical biomass distribution and dry matter content based on digital image analysis. , 2006, Annals of botany.

[70]  L. Xiong,et al.  Overexpressing a NAM, ATAF, and CUC (NAC) transcription factor enhances drought resistance and salt tolerance in rice , 2006, Proceedings of the National Academy of Sciences.

[71]  J. Sharpe,et al.  Visualizing Plant Development and Gene Expression in Three Dimensions Using Optical Projection Tomography[W] , 2006, The Plant Cell Online.

[72]  Byun-Woo Lee,et al.  Assessment of rice leaf growth and nitrogen status by hyperspectral canopy reflectance and partial least square regression , 2006 .

[73]  Bin Han,et al.  GS3, a major QTL for grain length and weight and minor QTL for grain width and thickness in rice, encodes a putative transmembrane protein , 2006, Theoretical and Applied Genetics.

[74]  J. Hanan,et al.  Rice morphogenesis and plant architecture: measurement, specification and the reconstruction of structural development by 3D architectural modelling. , 2005, Annals of botany.

[75]  Q. Lu,et al.  Localization of pms3, a gene for photoperiod-sensitive genic male sterility, to a 28.4-kb DNA fragment , 2005, Molecular Genetics and Genomics.

[76]  Roberto Oberti,et al.  Plant disease detection based on data fusion of hyper-spectral and multi-spectral fluorescence imaging using Kohonen maps , 2005, Real Time Imaging.

[77]  Noel D.G. White,et al.  Detection of internal wheat seed infestation by Rhyzopertha dominica using X-ray imaging , 2004 .

[78]  Gerald Kastberger,et al.  Infrared imaging technology and biological applications , 2003, Behavior research methods, instruments, & computers : a journal of the Psychonomic Society, Inc.

[79]  J. Medford,et al.  In vivo three‐dimensional imaging of plants with optical coherence microscopy , 2002, Journal of microscopy.

[80]  R. Reski,et al.  Plant functional genomics , 2002, Naturwissenschaften.

[81]  M. Natsuga,et al.  Development of an automatic rice-quality inspection system , 2001 .