A Review of Imaging Techniques for Plant Phenotyping

Given the rapid development of plant genomic technologies, a lack of access to plant phenotyping capabilities limits our ability to dissect the genetics of quantitative traits. Effective, high-throughput phenotyping platforms have recently been developed to solve this problem. In high-throughput phenotyping platforms, a variety of imaging methodologies are being used to collect data for quantitative studies of complex traits related to the growth, yield and adaptation to biotic or abiotic stress (disease, insects, drought and salinity). These imaging techniques include visible imaging (machine vision), imaging spectroscopy (multispectral and hyperspectral remote sensing), thermal infrared imaging, fluorescence imaging, 3D imaging and tomographic imaging (MRT, PET and CT). This paper presents a brief review on these imaging techniques and their applications in plant phenotyping. The features used to apply these imaging techniques to plant phenotyping are described and discussed in this review.

[1]  Hartmut K. Lichtenthaler,et al.  Principles and characteristics of multi-colour fluorescence imaging of plants , 1998 .

[2]  Z. Niu,et al.  Identification of yellow rust in wheat using in-situ spectral reflectance measurements and airborne hyperspectral imaging , 2007, Precision Agriculture.

[3]  D. Straeten,et al.  Imaging techniques and the early detection of plant stress. , 2000, Trends in plant science.

[4]  Hiroshi Uchida,et al.  Real Time Visualization of 13N-Translocation in Rice under Different Environmental Conditions Using Positron Emitting Tracer Imaging System , 2001 .

[5]  P. Schulze-Lefert,et al.  Metabolic consequences of susceptibility and resistance (race-specific and broad-spectrum) in barley leaves challenged with powdery mildew. , 2006, Plant, cell & environment.

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

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

[8]  F. Schmid,et al.  Analytical model for long-distance tracer-transport in plants. , 2011, Journal of theoretical biology.

[9]  J. Araus,et al.  Estimation of grain yield by near-infrared reflectance spectroscopy in durum wheat , 2004, Euphytica.

[10]  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.

[11]  H. Jones,et al.  Remote Sensing of Vegetation: Principles, Techniques, and Applications , 2010 .

[12]  H. Hilhorst,et al.  GERMINATOR: a software package for high-throughput scoring and curve fitting of Arabidopsis seed germination. , 2010, The Plant journal : for cell and molecular biology.

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

[14]  A. Henry,et al.  Taking transgenic rice drought screening to the field. , 2013, Journal of experimental botany.

[15]  Willem Kruijer,et al.  High throughput screening with chlorophyll fluorescence imaging and its use in crop improvement. , 2012, Current opinion in biotechnology.

[16]  Peter J. Gregory,et al.  Measuring root traits in barley (Hordeum vulgare ssp. vulgare and ssp. spontaneum) seedlings using gel chambers, soil sacs and X-ray microtomography , 2009, Plant and Soil.

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

[18]  H. S. Wolff,et al.  iRun: Horizontal and Vertical Shape of a Region-Based Graph Compression , 2022, Sensors.

[19]  H. Jones,et al.  Combining thermal and visible imagery for estimating canopy temperature and identifying plant stress. , 2004, Journal of experimental botany.

[20]  Dominique Van Der Straeten,et al.  Chlorophyll fluorescence imaging for disease-resistance screening of sugar beet , 2007, Plant Cell Tissue and Organ Culture.

[21]  N. Baker,et al.  Rapid, Noninvasive Screening for Perturbations of Metabolism and Plant Growth Using Chlorophyll Fluorescence Imaging1 , 2003, Plant Physiology.

[22]  Frank J Vergeldt,et al.  MRI of long-distance water transport: a comparison of the phloem and xylem flow characteristics and dynamics in poplar, castor bean, tomato and tobacco. , 2006, Plant, cell & environment.

[23]  Axel Munack,et al.  Test of a 3 D Time of Flight Camera for Shape Measurements of Plants , 2010 .

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

[25]  H. Poorter,et al.  Phenotyping plants: genes, phenes and machines. , 2012, Functional plant biology : FPB.

[26]  Ana I. Caño-Delgado,et al.  The genetic basis of fruit morphology in horticultural crops: lessons from tomato and melon. , 2013, Journal of experimental botany.

[27]  Stefan de Folter,et al.  A simple and efficient method for isolating small RNAs from different plant species , 2011, Plant Methods.

[28]  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.

[29]  B. Mueller‐Roeber,et al.  A growth phenotyping pipeline for Arabidopsis thaliana integrating image analysis and rosette area modeling for robust quantification of genotype effects. , 2011, The New phytologist.

[30]  Andrea Matros,et al.  Clustering of crop phenotypes by means of hyperspectral signatures using artificial neural networks , 2010, 2010 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing.

[31]  U. Schurr,et al.  Environmental effects on spatial and temporal patterns of leaf and root growth. , 2009, Annual review of plant biology.

[32]  W. Philips,et al.  Rosette Tracker: An Open Source Image Analysis Tool for Automatic Quantification of Genotype Effects1[C][W] , 2012, Plant Physiology.

[33]  S. Merlot,et al.  Use of infrared thermal imaging to isolate Arabidopsis mutants defective in stomatal regulation. , 2002, The Plant journal : for cell and molecular biology.

[34]  Neeraj Dilbaghi,et al.  Evaluating vegetation indices for precision phenotyping of quantitative stripe rust reaction in wheat , 2014 .

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

[36]  Jose A. Jiménez-Berni,et al.  Proximal Remote Sensing Buggies and Potential Applications for Field-Based Phenotyping , 2014 .

[37]  H. Jones,et al.  Thermal infrared imaging of crop canopies for the remote diagnosis and quantification of plant responses to water stress in the field. , 2009, Functional plant biology : FPB.

[38]  Juan Pedro Ferrio,et al.  Near infrared reflectance spectroscopy as a potential surrogate method for the analysis of D13C in mature kernels of durum wheat , 2001 .

[39]  Sándor Lenk,et al.  Multicolor fluorescence imaging for early detection of the hypersensitive reaction to tobacco mosaic virus. , 2007, Journal of plant physiology.

[40]  Angeles Calatayud,et al.  Applications of chlorophyll fluorescence imaging technique in horticultural research: A review , 2012 .

[41]  M. Watt,et al.  Non-destructive quantification of cereal roots in soil using high-resolution X-ray tomography. , 2012, Journal of experimental botany.

[42]  D. Rousseau,et al.  High throughput quantitative phenotyping of plant resistance using chlorophyll fluorescence image analysis , 2013, Plant Methods.

[43]  Geoffrey M. Henebry,et al.  Making Sense of Remotely Sensing Vegetation , 2011 .

[44]  L. Serrano,et al.  Assessment of grape yield and composition using the reflectance based Water Index in Mediterranean rainfed vineyards , 2012 .

[45]  Y. Cohen,et al.  Use of thermal and visible imagery for estimating crop water status of irrigated grapevine. , 2006, Journal of experimental botany.

[46]  J. Heard,et al.  Drought tolerance through biotechnology: improving translation from the laboratory to farmers' fields. , 2012, Current opinion in biotechnology.

[47]  José Luis Araus,et al.  Near-Infrared Reflectance Spectroscopy (NIRS) assessment of δ(18)O and nitrogen and ash contents for improved yield potential and drought adaptation in maize. , 2011, Journal of agricultural and food chemistry.

[48]  R. Klose,et al.  Usability study of 3 D Time-of-Flight cameras for automatic plant phenotyping , 2009 .

[49]  G. Noga,et al.  Quantum yield of non-regulated energy dissipation in PSII (Y(NO)) for early detection of leaf rust (Puccinia triticina) infection in susceptible and resistant wheat (Triticum aestivum L.) cultivars , 2010, Precision Agriculture.

[50]  Witold F. Krajewski,et al.  Three-dimensional digital model of a maize plant. , 2010 .

[51]  Jeffrey Ross-Ibarra,et al.  Genetic Architecture of Maize Kernel Composition in the Nested Association Mapping and Inbred Association Panels1[W] , 2011, Plant Physiology.

[52]  Gustavo André Colombo,et al.  Phenotyping of maize genotypes under aluminum stress and its correlation with grain yield. , 2014 .

[53]  H. Scharr,et al.  A stereo imaging system for measuring structural parameters of plant canopies. , 2007, Plant, cell & environment.

[54]  Jian Jin,et al.  Corn plant sensing using real‐time stereo vision , 2009, J. Field Robotics.

[55]  Byun-Woo Lee,et al.  Estimation of rice growth and nitrogen nutrition status using color digital camera image analysis , 2013 .

[56]  Gary Stacey,et al.  Phenotypic and Genomic Analyses of a Fast Neutron Mutant Population Resource in Soybean1[W][OA] , 2011, Plant Physiology.

[57]  H. Nilsson.  Remote sensing and image analysis in plant pathology. , 1995, Annual review of phytopathology.

[58]  Xiaolong Yan,et al.  3D reconstruction and dynamic modeling of root architecture in situ and its application to crop phosphorus research. , 2009, The Plant journal : for cell and molecular biology.

[59]  Qin Zhang,et al.  Creation of Three-dimensional Crop Maps based on Aerial Stereoimages , 2005 .

[60]  S. Rolfe,et al.  Chlorophyll fluorescence imaging of plant–pathogen interactions , 2010, Protoplasma.

[61]  Roger Meder,et al.  Quantitative dynamics of stem water soluble carbohydrates in wheat can be monitored in the field using hyperspectral reflectance , 2014 .

[62]  Yafit Cohen,et al.  Fusion of IR and Multispectral Images in the Visible Range for Empirical and Model Based Mapping of Crop Water Status , 2006 .

[63]  Uwe Rascher,et al.  Spatio-temporal variations of photosynthesis: the potential of optical remote sensing to better understand and scale light use efficiency and stresses of plant ecosystems , 2008, Precision Agriculture.

[64]  Andrew P. French,et al.  Plant Image Analysis , 2015 .

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

[66]  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.

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

[68]  Thomas Altmann,et al.  Dynamic ¹³C/¹ H NMR imaging uncovers sugar allocation in the living seed. , 2011, Plant biotechnology journal.

[69]  John A. Gamon,et al.  Monitoring drought effects on vegetation water content and fluxes in chaparral with the 970 nm water band index , 2006 .

[70]  P. Armstrong,et al.  High-Throughput Near-Infrared Reflectance Spectroscopy for Predicting Quantitative and Qualitative Composition Phenotypes of Individual Maize Kernels , 2009 .

[71]  E. Fereres,et al.  Using high resolution UAV thermal imagery to assess the variability in the water status of five fruit tree species within a commercial orchard , 2013, Precision Agriculture.

[72]  L. Xiong,et al.  Plant phenomics and high-throughput phenotyping: accelerating rice functional genomics using multidisciplinary technologies. , 2013, Current opinion in plant biology.

[73]  Tsuguo Okamoto,et al.  Automatic diagnosis of plant disease: Recognition between healthy and diseased leaf , 1999 .

[74]  Elizabeth Pattey,et al.  Retrieval of leaf area index from top-of-canopy digital photography over agricultural crops , 2010 .

[75]  Tony E Grift,et al.  High-throughput phenotyping technology for maize roots , 2011 .

[76]  C. Glasbey,et al.  SPICY: towards automated phenotyping of large pepper plants in the greenhouse. , 2012, Functional plant biology : FPB.

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

[78]  Chris A. Glasbey,et al.  Combining Stereo and Time-of-Flight Images with Application to Automatic Plant Phenotyping , 2011, SCIA.

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

[80]  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.

[81]  Svend Christensen,et al.  Development of a Mobile Multispectral Imaging Platform for Precise Field Phenotyping , 2014 .

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

[83]  James S. Schepers,et al.  Measuring Chlorophyll Content in Corn Leaves with Differing Nitrogen Levels and Relative Water Content , 2019 .

[84]  B. Mistele,et al.  Can changes in leaf water potential be assessed spectrally? , 2011, Functional plant biology : FPB.

[85]  J. Mayer,et al.  Infrared thermal sensing of plant canopies as a screening technique for dehydration avoidance in wheat , 1982 .

[86]  Konrad Walus,et al.  Delivering high-resolution landmarks using inkjet micropatterning for spatial monitoring of leaf expansion , 2011, Plant Methods.

[87]  K. P. Pruessmann,et al.  Analysis of nickel concentration profiles around the roots of the hyperaccumulator plant Berkheya coddii using MRI and numerical simulations , 2010, Plant and Soil.

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

[89]  C. Osmond,et al.  Infection with Phloem Limited Abutilon Mosaic Virus Causes Localized Carbohydrate Accumulation in Leaves of Abutilon striatum: Relationships to Symptom Development and Effects on Chlorophyll Fluorescence Quenching During Photosynthetic Induction , 2000 .

[90]  Luis Alonso,et al.  Remote sensing of solar-induced chlorophyll fluorescence: Review of methods and applications , 2009 .

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

[92]  P. Foucher,et al.  Morphological Image Analysis for the Detection of Water Stress in Potted Forsythia , 2004 .

[93]  Klaus Palme,et al.  SHORT-ROOT Regulates Primary, Lateral, and Adventitious Root Development in Arabidopsis1[C][W][OA] , 2010, Plant Physiology.

[94]  Robert Reis,et al.  Evaluation of phenotyping methods for maize , 2014 .

[95]  Arno Ruckelshausen,et al.  BreedVision — A Multi-Sensor Platform for Non-Destructive Field-Based Phenotyping in Plant Breeding , 2013, Sensors.

[96]  Karl Haro von Mogel,et al.  Taking the into the Field , 2013 .

[97]  F. Cellini,et al.  Engineered drought tolerance in tomato plants is reflected in chlorophyll fluorescence emission. , 2012, Plant science : an international journal of experimental plant biology.

[98]  Didier Demilly,et al.  QTL analysis of seed germination and pre-emergence growth at extreme temperatures in Medicago truncatula , 2010, Theoretical and Applied Genetics.

[99]  Tao Cheng,et al.  Spectroscopic determination of leaf water content using continuous wavelet analysis , 2010, 2010 IEEE International Geoscience and Remote Sensing Symposium.

[100]  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.

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

[102]  Joan Ramón Rosell Polo,et al.  A tractor-mounted scanning LIDAR for the non-destructive measurement of vegetative volume and surface area of tree-row plantations: A comparison with conventional destructive measurements , 2009 .

[103]  R. Laxman,et al.  Phenotyping Horticultural Crops for Abiotic Stress Tolerance , 2013 .

[104]  Luis Alonso,et al.  CEFLES2: the remote sensing component to quantify photosynthetic efficiency from the leaf to the region by measuring sun-induced fluorescence in the oxygen absorption bands. , 2009 .

[105]  Nobuo Ezaki,et al.  Detection of Wilt by Analyzing Color and Stereo Vision Data of Plant , 2007, MIRAGE.

[106]  F. Gilmer,et al.  Non-invasive determination of plant biomass with microwave resonators. , 2009, Plant, cell & environment.

[107]  D. Straeten,et al.  Seeing is believing: imaging techniques to monitor plant health. , 2001, Biochimica et biophysica acta.

[108]  Herbert Kaplan,et al.  Practical Applications of Infrared Thermal Sensing and Imaging Equipment , 1993 .

[109]  H. Scharr,et al.  GROWSCREEN-Rhizo is a novel phenotyping robot enabling simultaneous measurements of root and shoot growth for plants grown in soil-filled rhizotrons. , 2012, Functional plant biology : FPB.

[110]  E. J. Morton,et al.  Non-invasive imaging of roots with high resolution X-ray micro-tomography , 2003, Plant and Soil.

[111]  Andrew K. Skidmore,et al.  Evaluation of three proposed indices for the retrieval of leaf water content from the mid-wave infrared (2–6 μm) spectra , 2013 .

[112]  D. Leister,et al.  Large-scale evaluation of plant growth in Arabidopsis thaliana by non-invasive image analysis , 1999 .

[113]  S. Ollinger.  Sources of variability in canopy reflectance and the convergent properties of plants. , 2011, The New phytologist.

[114]  Arno Ruckelshausen,et al.  Plant moisture measurement in field trials based on NIR spectral imaging a feasibility study , 2010 .

[115]  Bani K. Mallick,et al.  Hyperspectral remote sensing of plant biochemistry using Bayesian model averaging with variable and band selection , 2013 .

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

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

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

[119]  Lie Tang,et al.  Automatic inter-plant spacing sensing at early growth stages using a 3D vision sensor , 2012 .

[120]  E. B. Knipling.  Physical and physiological basis for the reflectance of visible and near-infrared radiation from vegetation , 1970 .

[121]  S. Rolfe,et al.  Chlorophyll fluorescence imaging as tool for understanding the impact of fungal diseases on plant performance: a phenomics perspective. , 2009, Functional plant biology : FPB.

[122]  B. Pogson,et al.  A rapid, non-invasive procedure for quantitative assessment of drought survival using chlorophyll fluorescence , 2008, Plant Methods.

[123]  H. Lichtenthaler,et al.  Multispectral fluorescence and reflectance imaging at the leaf level and its possible applications. , 2006, Journal of experimental botany.

[124]  Y. Miao,et al.  EVALUATION OF TERRESTRIAL LASER SCANNING FOR RICE GROWTH MONITORING , 2012 .

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

[126]  Sergi Foix,et al.  Indoor and outdoor depth imaging of leaves with time-of-flight and stereo vision sensors: Analysis and comparison , 2014 .

[127]  O. Matsuda,et al.  Hyperspectral Imaging Techniques for Rapid Identification of Arabidopsis Mutants with Altered Leaf Pigment Status , 2012, Plant & cell physiology.

[128]  Hiroshi Uchida,et al.  52Fe Translocation in Barley as Monitored by a Positron-Emitting Tracer Imaging System (PETIS): Evidence for the Direct Translocation of Fe from Roots to Young Leaves via Phloem , 2008, Plant & cell physiology.

[129]  Chris Moran,et al.  3D reconstruction and quantification of macropores using X-ray computed tomography and image analysis , 2002 .

[130]  K. Omasa,et al.  3D monitoring spatio-temporal effects of herbicide on a whole plant using combined range and chlorophyll a fluorescence imaging. , 2009, Functional plant biology : FPB.

[131]  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 .

[132]  Jean-Luc Jannink,et al.  Genomic selection in plant breeding: from theory to practice. , 2010, Briefings in functional genomics.

[133]  Wilco Ligterink,et al.  Visualizing the Genetic Landscape of Arabidopsis Seed Performance1[W][OA] , 2011, Plant Physiology.

[134]  W. Broekaert,et al.  TraitMill: a Discovery Engine for Identifying Yield-enhancement Genes in Cereals , 2010 .

[135]  M. Tester,et al.  Mechanisms of salinity tolerance. , 2008, Annual review of plant biology.

[136]  Dirk Walther,et al.  Data management pipeline for plant phenotyping in a multisite project. , 2012, Functional plant biology : FPB.

[137]  D. Ehlert,et al.  Rapid Mapping of the Leaf Area Index in Agricultural Crops , 2011 .

[138]  K. Chenu,et al.  PHENOPSIS, an automated platform for reproducible phenotyping of plant responses to soil water deficit in Arabidopsis thaliana permitted the identification of an accession with low sensitivity to soil water deficit. , 2006, The New phytologist.

[139]  Felix B. Fritschi,et al.  Ground‐Based Digital Imaging as a Tool to Assess Soybean Growth and Yield , 2014 .

[140]  Clement Atzberger,et al.  LAI and chlorophyll estimation for a heterogeneous grassland using hyperspectral measurements , 2008 .

[141]  Kikuo Fujimura,et al.  Development of a Controlled Vocabulary and Software Application to Analyze Fruit Shape Variation in Tomato and Other Plant Species1[W] , 2006, Plant Physiology.

[142]  Henk Van As,et al.  MRI of plants and foods. , 2013, Journal of magnetic resonance.

[143]  Anming Bao,et al.  Estimation of leaf water content in cotton by means of hyperspectral indices , 2013 .

[144]  E. Oerke,et al.  Nuclear magnetic resonance: a tool for imaging belowground damage caused by Heterodera schachtii and Rhizoctonia solani on sugar beet , 2011, Journal of experimental botany.

[145]  Jean-Luc Jannink,et al.  Genomic selection in plant breeding. , 2014, Methods in molecular biology.

[146]  B. Andrieu,et al.  Computer stereo plotting for 3-D reconstruction of a maize canopy , 1995 .

[147]  Sebastian Riedel,et al.  Automated Analysis of Barley Organs Using 3D Laser Scanning: An Approach for High Throughput Phenotyping , 2014, Sensors.

[148]  Pankaj Kumar,et al.  Root phenotyping by root tip detection and classification through statistical learning , 2014, Plant and Soil.

[149]  J. Eitel,et al.  LiDAR based biomass and crop nitrogen estimates for rapid, non-destructive assessment of wheat nitrogen status , 2014 .

[150]  Wolfram Spreer,et al.  Rapid phenotyping of different maize varieties under drought stress by using thermal images , 2013 .

[151]  Jeffrey W. White,et al.  Development and evaluation of a field-based high-throughput phenotyping platform. , 2013, Functional plant biology : FPB.

[152]  Ronald L. Phillips,et al.  Mobilizing Science to Break Yield Barriers , 2010 .

[153]  Philip A. Townsend,et al.  Leaf optical properties reflect variation in photosynthetic metabolism and its sensitivity to temperature , 2011, Journal of experimental botany.

[154]  J. Araus,et al.  Phenotyping maize for adaptation to drought , 2012, Front. Physio..

[155]  R. Richards,et al.  Breeding for improved water productivity in temperate cereals: phenotyping, quantitative trait loci, markers and the selection environment , 2010 .

[156]  J. Abe.  Roots: The Dynamic Interface between Plants and the Earth , 2003, Developments in Plant and Soil Sciences.

[157]  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 .

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

[159]  Sharon A. Robinson,et al.  Concepts of plant biotic stress. Some insights into the stress physiology of virus‐infected plants, from the perspective of photosynthesis , 1997 .

[160]  A. Walter,et al.  REVIEW: PART OF A HIGHLIGHT ON BREEDING STRATEGIES FOR FORAGE AND GRASS IMPROVEMENT Advanced phenotyping offers opportunities for improved breeding of forage and turf species , 2012 .

[161]  Saoirse R Tracy,et al.  The X-factor: visualizing undisturbed root architecture in soils using X-ray computed tomography. , 2010, Journal of experimental botany.

[162]  Albrecht E. Melchinger,et al.  High-throughput non-destructive biomass determination during early plant development in maize under field conditions , 2011 .

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

[164]  H. Lichtenthaler,et al.  Imaging of the Blue, Green, and Red Fluorescence Emission of Plants: An Overview , 2000, Photosynthetica.

[165]  J. Perret,et al.  Non-destructive visualization and quantification of roots using computed tomography , 2007 .

[166]  M. R. Thorpe,et al.  Non-invasive approaches for phenotyping of enhanced performance traits in bean. , 2011, Functional plant biology : FPB.

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

[168]  George Azzari,et al.  Rapid Characterization of Vegetation Structure with a Microsoft Kinect Sensor , 2013, Sensors.

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

[170]  H. Jones,et al.  Multi‐sensor plant imaging: Towards the development of a stress‐catalogue , 2009, Biotechnology journal.

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

[172]  Jochen C Reif,et al.  Novel throughput phenotyping platforms in plant genetic studies. , 2007, Trends in plant science.

[173]  B. Mistele,et al.  Spectral measurements of the total aerial N and biomass dry weight in maize using a quadrilateral-view optic , 2008 .

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

[175]  Shiv O. Prasher,et al.  Advances in the acquisition and analysis of CT scan data to isolate a crop root system from the soil medium and quantify root system complexity in 3-D space , 2006 .

[176]  Carme Torras,et al.  3D modelling of leaves from color and ToF data for robotized plant measuring , 2011, 2011 IEEE International Conference on Robotics and Automation.

[177]  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.

[178]  K. Omasa,et al.  Estimating vertical plant area density profile and growth parameters of a wheat canopy at different growth stages using three-dimensional portable lidar imaging , 2009 .

[179]  Nobuo Ezaki,et al.  Plant Recognition by Integrating Color and Range Data Obtained Through Stereo Vision , 2005, J. Adv. Comput. Intell. Intell. Informatics.

[180]  P. Lootens,et al.  Evaluation of cold stress of young industrial chicory (Cichorium intybus L.) by chlorophyll a fluorescence imaging. II. Dark relaxation kinetics , 2011, Photosynthetica.

[181]  F. Baret,et al.  A semi-automatic system for high throughput phenotyping wheat cultivars in-field conditions: description and first results. , 2012, Functional plant biology : FPB.

[182]  N. Grassly,et al.  United Nations Department of Economic and Social Affairs/population Division , 2022 .

[183]  Yao-Cheng Lin,et al.  A rice phenomics study—phenotype scoring and seed propagation of a T-DNA insertion-induced rice mutant population , 2007, Plant Molecular Biology.

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

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

[186]  W. Stuppy,et al.  Three-dimensional analysis of plant structure using high-resolution X-ray computed tomography. , 2003, Trends in plant science.

[187]  R. S. Rutherford,et al.  NEAR-INFRARED REFLECTANCE (NIR) SPECTROSCOPY AS A HIGH-THROUGHPUT SCREENING TOOL FOR PEST AND DISEASE RESISTANCE IN A SUGARCANE BREEDING PROGRAMME , 2014 .

[188]  S. Danielsen,et al.  Evaluation of disease assessment methods in quinoa for their ability to predict yield loss caused by downy mildew , 2004 .

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

[190]  Thomas Altmann,et al.  Dynamic 13 C ⁄ 1 H NMR imaging uncovers sugar allocation in the living seed , 2011 .

[191]  H. Jones,et al.  Monitoring and screening plant populations with combined thermal and chlorophyll fluorescence imaging. , 2007, Journal of experimental botany.

[192]  H. Jones,et al.  Remote Sensing of Vegetation: Principles, Techniques, and Applications , 2010 .

[193]  Gustavo A. Slafer,et al.  Can wheat yield be assessed by early measurements of Normalized Difference Vegetation Index , 2007 .

[194]  K. Omasa,et al.  3D lidar imaging for detecting and understanding plant responses and canopy structure. , 2006, Journal of experimental botany.

[195]  J. L. Araus,et al.  Using vegetation indices derived from conventional digital cameras as selection criteria for wheat breeding in water-limited environments , 2007 .

[196]  Eva Rosenqvist,et al.  Applications of chlorophyll fluorescence can improve crop production strategies: an examination of future possibilities. , 2004, Journal of experimental botany.

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

[198]  Wanneng Yang,et al.  Rice panicle length measuring system based on dual-camera imaging , 2013 .

[199]  J. Araus,et al.  Infrared Thermal Imaging as a Rapid Tool for Identifying Water-Stress Tolerant Maize Genotypes of Different Phenology , 2013 .

[200]  José Crossa,et al.  High-throughput phenotyping and genomic selection: the frontiers of crop breeding converge. , 2012, Journal of integrative plant biology.

[201]  Hanno Scharr,et al.  Measuring Plant Root Growth , 2008, DAGM-Symposium.

[202]  M. McMullen,et al.  Genetic Properties of the Maize Nested Association Mapping Population , 2009, Science.

[203]  Llorenç Cabrera-Bosquet,et al.  NDVI as a potential tool for predicting biomass, plant nitrogen content and growth in wheat genotypes subjected to different water and nitrogen conditions , 2011 .

[204]  Jevin D. West,et al.  Dynamics of stomatal patches for a single surface of Xanthium strumarium L. leaves observed with fluorescence and thermal images , 2005 .

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