Lights, camera, action: high-throughput plant phenotyping is ready for a close-up.

[1]  A. M. Edwards,et al.  Utilization of a high-throughput shoot imaging system to examine the dynamic phenotypic responses of a C4 cereal crop plant to nitrogen and water deficiency over time , 2015, Journal of experimental botany.

[2]  Leonidas Deligiannidis,et al.  Emerging Trends in Image Processing, Computer Vision and Pattern Recognition , 2014 .

[3]  C. Klukas,et al.  Dissecting the Phenotypic Components of Crop Plant Growth and Drought Responses Based on High-Throughput Image Analysis[W][OPEN] , 2014, Plant Cell.

[4]  Michael P. Pound,et al.  Automated Recovery of Three-Dimensional Models of Plant Shoots from Multiple Color Images1[C][W][OPEN] , 2014, Plant Physiology.

[5]  A. Raftery,et al.  World population stabilization unlikely this century , 2014, Science.

[6]  D. Chitwood.  Imitation, Genetic Lineages, and Time Influenced the Morphological Evolution of the Violin , 2014, PloS one.

[7]  L. Xiong,et al.  Combining high-throughput phenotyping and genome-wide association studies to reveal natural genetic variation in rice , 2014, Nature Communications.

[8]  Ioannis Xenarios,et al.  Differentially Phased Leaf Growth and Movements in Arabidopsis Depend on Coordinated Circadian and Light Regulation[W] , 2014, Plant Cell.

[9]  Abhiram Das,et al.  Image-Based High-Throughput Field Phenotyping of Crop Roots1[W][OPEN] , 2014, Plant Physiology.

[10]  Yin Hoon Chew,et al.  Multiscale digital Arabidopsis predicts individual organ and whole-organism growth , 2014, Proceedings of the National Academy of Sciences.

[11]  Jie Peng,et al.  Resolving Distinct Genetic Regulators of Tomato Leaf Shape within a Heteroblastic and Ontogenetic Context[W][OPEN] , 2014, Plant Cell.

[12]  Sotirios A. Tsaftaris,et al.  Image-based plant phenotyping with incremental learning and active contours , 2014, Ecol. Informatics.

[13]  M. Tester,et al.  Image-based phenotyping for non-destructive screening of different salinity tolerance traits in rice , 2014, Rice.

[14]  Hanno Scharr,et al.  Annotated Image Datasets of Rosette Plants , 2014 .

[15]  Steve A. Kay,et al.  Daily Changes in Temperature, Not the Circadian Clock, Regulate Growth Rate in Brachypodium distachyon , 2014, PloS one.

[16]  M. Tester,et al.  High-Throughput Phenotyping to Detect Drought Tolerance QTL in Wild Barley Introgression Lines , 2014, PloS one.

[17]  Christian Klukas,et al.  Integrated Analysis Platform: An Open-Source Information System for High-Throughput Plant Phenotyping1[C][W][OPEN] , 2014, Plant Physiology.

[18]  Lutz Plümer,et al.  Low-Cost 3D Systems: Suitable Tools for Plant Phenotyping , 2014, Sensors.

[19]  Stewart J. Cohen,et al.  Climate Change 2014: Impacts,Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change , 2014 .

[20]  J. Araus,et al.  Field high-throughput phenotyping: the new crop breeding frontier. , 2014, Trends in plant science.

[21]  Yuhua Jiao,et al.  Functional approach to high-throughput plant growth analysis , 2013, BMC Systems Biology.

[22]  P. Pardey,et al.  Public agricultural R&D over the past half century: an emerging new world order , 2013 .

[23]  M. Livny,et al.  High-Throughput Computer Vision Introduces the Time Axis to a Quantitative Trait Map of a Plant Growth Response , 2013, Genetics.

[24]  Daniel Cohen-Or,et al.  Analyzing growing plants from 4D point cloud data , 2013, ACM Trans. Graph..

[25]  Xavier Draye,et al.  An online database for plant image analysis software tools , 2013, Plant Methods.

[26]  Heiner Kuhlmann,et al.  Surface feature based classification of plant organs from 3D laserscanned point clouds for plant phenotyping , 2013, BMC Bioinformatics.

[27]  L. Rieseberg,et al.  Agriculture: Feeding the future , 2013, Nature.

[28]  J. Foley,et al.  Yield Trends Are Insufficient to Double Global Crop Production by 2050 , 2013, PloS one.

[29]  Ulrich Schurr,et al.  Future scenarios for plant phenotyping. , 2013, Annual review of plant biology.

[30]  O. Loudet,et al.  Phenoscope: an automated large-scale phenotyping platform offering high spatial homogeneity. , 2013, The Plant journal : for cell and molecular biology.

[31]  Paul R Zurek,et al.  3D phenotyping and quantitative trait locus mapping identify core regions of the rice genome controlling root architecture , 2013, Proceedings of the National Academy of Sciences.

[32]  Ming C. Lin,et al.  Example-guided physically based modal sound synthesis , 2013, ACM Trans. Graph..

[33]  L. Plümer,et al.  Development of spectral indices for detecting and identifying plant diseases , 2013 .

[34]  Sean R. Davis,et al.  NCBI GEO: archive for functional genomics data sets—update , 2012, Nucleic Acids Res..

[35]  I. Xenarios,et al.  Measuring the diurnal pattern of leaf hyponasty and growth in Arabidopsis - a novel phenotyping approach using laser scanning. , 2012, Functional plant biology : FPB.

[36]  K. Kersting,et al.  Early drought stress detection in cereals: simplex volume maximisation for hyperspectral image analysis. , 2012, Functional plant biology : FPB.

[37]  Jeffrey W. White,et al.  Field-based phenomics for plant genetics research , 2012 .

[38]  Anne-Katrin Mahlein,et al.  Recent advances in sensing plant diseases for precision crop protection , 2012, European Journal of Plant Pathology.

[39]  Philippe Lucidarme,et al.  On the use of depth camera for 3D phenotyping of entire plants , 2012 .

[40]  J. Borevitz,et al.  Natural Genetic Variation for Growth and Development Revealed by High-Throughput Phenotyping in Arabidopsis thaliana , 2012, G3: Genes | Genomes | Genetics.

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

[42]  P. Pardey,et al.  The Economic Returns to U.S. Public Agricultural Research , 2011 .

[43]  B. S. Manjunath,et al.  The iPlant Collaborative: Cyberinfrastructure for Plant Biology , 2011, Front. Plant Sci..

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

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

[46]  Amir H Assadi,et al.  Detection of a Gravitropism Phenotype in glutamate receptor-like 3.3 Mutants of Arabidopsis thaliana Using Machine Vision and Computation , 2010, Genetics.

[47]  Menachem Moshelion,et al.  Development of synchronized, autonomous, and self-regulated oscillations in transpiration rate of a whole tomato plant under water stress , 2010, Journal of experimental botany.

[48]  P. Benfey,et al.  Imaging and Analysis Platform for Automatic Phenotyping and Trait Ranking of Plant Root Systems1[W][OA] , 2010, Plant Physiology.

[49]  H. Scharr,et al.  Simultaneous phenotyping of leaf growth and chlorophyll fluorescence via GROWSCREEN FLUORO allows detection of stress tolerance in Arabidopsis thaliana and other rosette plants. , 2009, Functional plant biology : FPB.

[50]  Xavier Sirault,et al.  A new screening method for osmotic component of salinity tolerance in cereals using infrared thermography. , 2009, Functional plant biology : FPB.

[51]  D. M. Klaus,et al.  The assessment of leaf water content using leaf reflectance ratios in the visible, near‐, and short‐wave‐infrared , 2008 .

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

[53]  Robert Pless,et al.  Consistent Temporal Variations in Many Outdoor Scenes , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[54]  Sven Molkenstruck,et al.  Low-Cost Laser Range Scanner and Fast Surface Registration Approach , 2006, DAGM-Symposium.

[55]  W. Broekaert,et al.  Traitmill™: a functional genomics platform for the phenotypic analysis of cereals , 2006, Plant Genetic Resources.

[56]  Alex E. Lash,et al.  Gene Expression Omnibus: NCBI gene expression and hybridization array data repository , 2002, Nucleic Acids Res..