Perspectives in High-Throughput Phenotyping of Qualitative Traits at the Whole-Plant Level
暂无分享,去创建一个
Oksana Sytar | Marek Zivcak | Katarina Olsovska | Marian Brestic | O. Sytar | M. Brestič | K. Olšovská | M. Živčák | Marek Živčák
[1] C. A. Jaleel,et al. Understanding water deficit stress-induced changes in the basic metabolism of higher plants – biotechnologically and sustainably improving agriculture and the ecoenvironment in arid regions of the globe , 2009, Critical reviews in biotechnology.
[2] Yu. Pogoreltsev,et al. The Application , 2020, How to Succeed in the Academic Clinical Interview.
[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] Alisdair R. Fernie,et al. Profiling of diurnal patterns of metabolite and transcript abundance in potato (Solanum tuberosum) leaves , 2005, Planta.
[5] Novel resistance mechanism of barley chlorina f104 antenna mutant against photoinhibition: possible role of new identified chloroplastic cpNrp protein , 2015, Theoretical and Experimental Plant Physiology.
[6] Guowang Xu,et al. Effects of Short-Term High Temperature on Photosynthesis and Photosystem II Performance in Sorghum , 2011 .
[7] Ģ. Zaķis. Functional analysis of lignins and their derivatives , 1997 .
[8] L. Plümer,et al. Development of spectral indices for detecting and identifying plant diseases , 2013 .
[9] R. Verpoorte,et al. Ethnopharmacology and systems biology: a perfect holistic match. , 2005, Journal of ethnopharmacology.
[10] P. Kumar,et al. Metabolomics in agriculture. , 2012, Omics : a journal of integrative biology.
[11] Antonio Roberto Formaggio,et al. Narrow band spectral indexes for chlorophyll determination in soybean canopies [Glycine max (L.) Merril] , 2004 .
[12] J. Rowland,et al. Nondestructive analysis of senescence in mesophyll cells by spectral resolution of protein synthesis-dependent pigment metabolism. , 2008, The New phytologist.
[13] Paul J. Williams,et al. Indirect Detection of Fusarium Verticillioides in Maize (Zea mays L.) Kernels by near Infrared Hyperspectral Imaging , 2010 .
[14] Jianlong Li,et al. Assessing nutritional status of Festuca arundinacea by monitoring photosynthetic pigments from hyperspectral data , 2010 .
[15] G. A. Blackburn,et al. Hyperspectral remote sensing of plant pigments. , 2006, Journal of experimental botany.
[16] Shao Hongbo,et al. Investigation on dynamic changes of photosynthetic characteristics of 10 wheat (Triticum aestivum L.) genotypes during two vegetative-growth stages at water deficits. , 2005, Colloids and surfaces. B, Biointerfaces.
[17] Da-Wen Sun,et al. Principles and Applications of Hyperspectral Imaging in Quality Evaluation of Agro-Food Products: A Review , 2012, Critical reviews in food science and nutrition.
[18] S. Barsberg,et al. Environmental effects on the lignin model monomer, vanillyl alcohol, studied by Raman spectroscopy. , 2011, The journal of physical chemistry. B.
[19] Da-Wen Sun,et al. Hyperspectral imaging for food quality analysis and control , 2010 .
[20] Ranga B. Myneni,et al. Canopy spectral invariants, Part 2: Application to classification of forest types from hyperspectral data , 2011 .
[21] A. Gitelson,et al. Reflectance spectral features and non-destructive estimation of chlorophyll, carotenoid and anthocyanin content in apple fruit , 2003 .
[22] Xavier Draye,et al. An online database for plant image analysis software tools , 2013, Plant Methods.
[23] A. Gitelson,et al. Optical Properties and Nondestructive Estimation of Anthocyanin Content in Plant Leaves¶ , 2001, Photochemistry and photobiology.
[24] Jiewen Zhao,et al. Determination of total flavonoids content in fresh Ginkgo biloba leaf with different colors using near infrared spectroscopy. , 2012, Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy.
[25] Joost J B Keurentjes,et al. Genetic analysis of metabolome-phenotype interactions: from model to crop species. , 2013, Trends in genetics : TIG.
[26] I. Rao,et al. Phenotyping common beans for adaptation to drought , 2013, Front. Physiol..
[27] Hongbo Shao,et al. Applying hyperspectral imaging to explore natural plant diversity towards improving salt stress tolerance. , 2017, The Science of the total environment.
[28] E. Ryan,et al. Evaluation of diversity among common beans (Phaseolus vulgaris L.) from two centers of domestication using 'omics' technologies , 2010, BMC Genomics.
[29] K. Kersting,et al. Early drought stress detection in cereals: simplex volume maximisation for hyperspectral image analysis. , 2012, Functional plant biology : FPB.
[30] F. Tardieu,et al. The growths of leaves, shoots, roots and reproductive organs partly share their genetic control in maize plants. , 2013, Plant, cell & environment.
[31] Colm P. O'Donnell,et al. Hyperspectral imaging – an emerging process analytical tool for food quality and safety control , 2007 .
[32] Jonathan Cheung-Wai Chan,et al. Evaluation of random forest and adaboost tree-based ensemble classification and spectral band selection for ecotope mapping using airborne hyperspectral imagery , 2008 .
[33] Di Wu,et al. Advanced applications of hyperspectral imaging technology for food quality and safety analysis and assessment: A review — Part II: Applications , 2013 .
[34] C. Messina,et al. Yield-trait performance landscapes: from theory to application in breeding maize for drought tolerance. , 2011, Journal of experimental botany.
[35] S. Uma,et al. Phenotyping bananas for drought resistance , 2012, Front. Physio..
[36] A. Peirs,et al. Nondestructive measurement of fruit and vegetable quality by means of NIR spectroscopy: A review , 2007 .
[37] V. Vadez,et al. II.1.5 Phenotyping pearl millet for adaptation to drought , 2012, Front. Physio..
[38] Richard M Caprioli,et al. Imaging mass spectrometry: a new tool to investigate the spatial organization of peptides and proteins in mammalian tissue sections. , 2002, Current opinion in chemical biology.
[39] A. Hall. Phenotyping Cowpeas for Adaptation to Drought , 2012, Front. Physio..
[40] T. Astatkie,et al. Row and Plant Spacing Effects on Yield and Yield Components of Soya Bean Varieties Under Hot Humid Tropical Environment of Ethiopia , 2011 .
[41] Thomas Altmann,et al. Identification of enzymatic and regulatory genes of plant metabolism through QTL analysis in Arabidopsis. , 2011, Journal of plant physiology.
[42] A. Griffiths,et al. High-resolution dose–response screening using droplet-based microfluidics , 2011, Proceedings of the National Academy of Sciences of the United States of America.
[43] Tao Dong,et al. Recent Developments in Optical Detection Technologies in Lab-on-a-Chip Devices for Biosensing Applications , 2014, Sensors.
[44] Werner B. Herppich,et al. Hyperspectral and Chlorophyll Fluorescence Imaging for Early Detection of Plant Diseases, with Special Reference to Fusarium spec. Infections on Wheat , 2014 .
[45] Emanuela Gobbi,et al. Prediction of milled maize fumonisin contamination by multispectral image analysis. , 2010 .
[46] Ute Roessner,et al. Detection of QTL for metabolic and agronomic traits in wheat with adjustments for variation at genetic loci that affect plant phenology. , 2015, Plant science : an international journal of experimental plant biology.
[47] F. Tardieu,et al. Dissection and modelling of abiotic stress tolerance in plants. , 2010, Current opinion in plant biology.
[48] R. Jing,et al. Phenotyping for drought adaptation in wheat using physiological traits , 2012, Front. Physio..
[49] H. Shao,et al. Physiological Mechanisms for High Salt Tolerance in Wild Soybean (Glycine soja) from Yellow River Delta, China: Photosynthesis, Osmotic Regulation, Ion Flux and antioxidant Capacity , 2013, PloS one.
[50] Ulrich Schurr,et al. Future scenarios for plant phenotyping. , 2013, Annual review of plant biology.
[51] Jiseok Lim,et al. Micro-optical lens array for fluorescence detection in droplet-based microfluidics† †Electronic supplementary information (ESI) available: Supplementary Figures (S1 and S2). Supplementary movie 01: movie recorded by a high-speed camera without backlight illumination. Supplementary movie 02: movie re , 2013, Lab on a chip.
[52] L. Tran,et al. The Contribution of Buckwheat Genetic Resources to Health and Dietary Diversity , 2016, Current genomics.
[53] Søren Balling Engelsen,et al. High-throughput cereal metabolomics: Current analytical technologies, challenges and perspectives , 2014 .
[54] J. A. Schultz,et al. Direct tissue analysis of phospholipids in rat brain using MALDI-TOFMS and MALDI-ion mobility-TOFMS , 2005, Journal of the American Society for Mass Spectrometry.
[55] M. Livny,et al. High-Throughput Computer Vision Introduces the Time Axis to a Quantitative Trait Map of a Plant Growth Response , 2013, Genetics.
[56] Ulrich S Schubert,et al. Matrix-free UV-laser desorption/ionization (LDI) mass spectrometric imaging at the single-cell level: distribution of secondary metabolites of Arabidopsis thaliana and Hypericum species. , 2009, The Plant journal : for cell and molecular biology.
[57] P. Baranowski,et al. Detection of early bruises in apples using hyperspectral data and thermal imaging , 2012 .
[58] Digvir S. Jayas,et al. Classification of Wheat Kernels Using Near-Infrared Reflectance Hyperspectral Imaging , 2010 .
[59] N. Baker. Chlorophyll fluorescence: a probe of photosynthesis in vivo. , 2008, Annual review of plant biology.
[60] H. Craighead,et al. Micro- and nanomechanical sensors for environmental, chemical, and biological detection. , 2007, Lab on a chip.
[61] Colm P. O'Donnell,et al. Comparison of hyperspectral imaging with conventional RGB imaging for quality evaluation of Agaricus bisporus mushrooms , 2011 .
[62] A. Walter,et al. Plant phenotyping: from bean weighing to image analysis , 2015, Plant Methods.
[63] Anne-Katrin Mahlein,et al. Remote sensing to detect plant stress induced by Heterodera schachtii and Rhizoctonia solani in suga , 2011 .
[64] J. Weitz,et al. Breakthrough Technologies Image-Based High-Throughput Field Phenotyping of Crop Roots 1 [ W ] [ OPEN ] , 2014 .
[65] M. Tester,et al. Quantifying the three main components of salinity tolerance in cereals. , 2009, Plant, cell & environment.
[66] Amy M. Sheflin,et al. Harnessing the rhizosphere microbiome through plant breeding and agricultural management , 2012, Plant and Soil.
[67] S. Vermeulen,et al. Breeding Technologies to Increase Crop Production in a Changing World , 2010 .
[68] P. Thenkabail,et al. Hyperspectral Vegetation Indices and Their Relationships with Agricultural Crop Characteristics , 2000 .
[69] Kazuki Saito,et al. Dissection of genotype-phenotype associations in rice grains using metabolome quantitative trait loci analysis. , 2012, The Plant journal : for cell and molecular biology.
[70] A. Greenberg,et al. Next-generation phenotyping: requirements and strategies for enhancing our understanding of genotype–phenotype relationships and its relevance to crop improvement , 2013, Theoretical and Applied Genetics.
[71] D. Sims,et al. Relationships between leaf pigment content and spectral reflectance across a wide range of species, leaf structures and developmental stages , 2002 .
[72] Karl Rihaczek,et al. 1. WHAT IS DATA MINING? , 2019, Data Mining for the Social Sciences.
[73] Anne-Katrin Mahlein,et al. Hyperspectral imaging for small-scale analysis of symptoms caused by different sugar beet diseases , 2012, Plant Methods.
[74] Yuhua Jiao,et al. Functional approach to high-throughput plant growth analysis , 2013, BMC Systems Biology.
[75] Elfatih M. Abdel-Rahman,et al. Estimation of sugarcane leaf nitrogen concentration using in situ spectroscopy , 2010, Int. J. Appl. Earth Obs. Geoinformation.
[76] Jason S. Lupoi,et al. 1064 nm dispersive multichannel Raman spectroscopy for the analysis of plant lignin. , 2011, Analytica chimica acta.
[77] J. Bruinsma,et al. World agriculture towards 2030/2050: the 2012 revision , 2012 .
[78] L. Zhang,et al. Photosynthetic characterization of Jerusalem artichoke during leaf expansion , 2011, Acta Physiologiae Plantarum.
[79] 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.
[80] M. Ashton,et al. Accuracy assessments of hyperspectral waveband performance for vegetation analysis applications , 2004 .
[81] J. Koricheva,et al. Application of metabolomics to genotype and phenotype discrimination of birch trees grown in a long-term open-field experiment , 2008, Metabolomics.
[82] Abhiram Das,et al. Image-Based High-Throughput Field Phenotyping of Crop Roots1[W][OPEN] , 2014, Plant Physiology.
[83] T. Elder,et al. Lignin and Lignans Advances in Chemistry , 2010 .
[84] Chun-Chieh Yang,et al. The development of a simple multispectral algorithm for detection of fecal contamination on apples using a hyperspectral line-scan imaging system , 2011 .
[85] S. Fujimura,et al. Nondestructive measurement of chlorophyll pigment content in plant leaves from three-color reflectance and transmittance. , 1991, Applied optics.
[86] Geoff Smith,et al. Hyperspectral imaging for mapping sediment characteristics , 2003 .
[87] Weixing Cao,et al. Exploring hyperspectral bands and estimation indices for leaf nitrogen accumulation in wheat , 2010, Int. J. Appl. Earth Obs. Geoinformation.
[88] Malia A. Gehan,et al. Lights, camera, action: high-throughput plant phenotyping is ready for a close-up. , 2015, Current opinion in plant biology.
[89] U. Steiner,et al. Spectral signatures of sugar beet leaves for the detection and differentiation of diseases , 2010, Precision Agriculture.
[90] Yud-Ren Chen,et al. Machine vision technology for agricultural applications , 2002 .
[91] Nuria Aleixos,et al. Selection of Optimal Wavelength Features for Decay Detection in Citrus Fruit Using the ROC Curve and Neural Networks , 2013, Food and Bioprocess Technology.
[92] Christine Stone,et al. Chlorophyll content in eucalypt vegetation at the leaf and canopy scales as derived from high resolution spectral data. , 2003, Tree physiology.
[93] I. Filella,et al. Reflectance assessment of mite effects on apple trees , 1995 .
[94] J. Blasco,et al. Recent Advances and Applications of Hyperspectral Imaging for Fruit and Vegetable Quality Assessment , 2012, Food and Bioprocess Technology.
[95] E. Bauriegel,et al. Chlorophyll fluorescence imaging to facilitate breeding of Bremia lactucae-resistant lettuce cultivars. , 2014 .
[96] H. Shao,et al. Global plant-responding mechanisms to salt stress: physiological and molecular levels and implications in biotechnology , 2015, Critical reviews in biotechnology.
[97] Paul J. Williams,et al. Investigation of fungal development in maize kernels using NIR hyperspectral imaging and multivariate data analysis , 2012 .
[98] M. Brestič,et al. Dissection of Photosynthetic Electron Transport Process in Sweet Sorghum under Heat Stress , 2013, PloS one.
[99] Jean-Marcel Ribaut,et al. Drought phenotyping in crops: From theory to practice. , 2014 .
[100] O. Sytar,et al. The application of multiplex fluorimetric sensor for the analysis of flavonoids content in the medicinal herbs family Asteraceae, Lamiaceae, Rosaceae , 2015, Biological Research.
[101] H. Mock,et al. Mass Spectrometry Based Imaging Techniques for Spatially Resolved Analysis of Molecules , 2013, Front. Plant Sci..
[102] M. Shahin,et al. Original paper: Detection of Fusarium damaged kernels in Canada Western Red Spring wheat using visible/near-infrared hyperspectral imaging and principal component analysis , 2011 .
[103] M. Schwarz,et al. Recent developments in detection methods for microfabricated analytical devices. , 2001, Lab on a chip.
[104] P. Chaurand,et al. Organic ion imaging of biological tissue with secondary ion mass spectrometry and matrix-assisted laser desorption/ionization. , 2001, Journal of mass spectrometry : JMS.
[105] Qin Zhang,et al. A Review of Imaging Techniques for Plant Phenotyping , 2014, Sensors.
[106] Alisdair R Fernie,et al. Plant metabolomics: towards biological function and mechanism. , 2006, Trends in plant science.
[107] Falk Schreiber,et al. HTPheno: An image analysis pipeline for high-throughput plant phenotyping , 2011, BMC Bioinformatics.
[108] D. M. Klaus,et al. The assessment of leaf water content using leaf reflectance ratios in the visible, near‐, and short‐wave‐infrared , 2008 .
[109] D. P. Ariana,et al. Evaluation of internal defect and surface color of whole pickles using hyperspectral imaging. , 2010 .
[110] R. Varshney,et al. Phenotyping Chickpeas and Pigeonpeas for Adaptation to Drought , 2012, Front. Physio..
[111] M. P. Davey,et al. Metabolomics in plant environmental physiology. , 2013, Journal of experimental botany.
[112] A. Fernie,et al. Metabolic profiling and biochemical phenotyping of plant systems , 2002, Plant Cell Reports.
[113] K. Soudani,et al. Calibration and validation of hyperspectral indices for the estimation of broadleaved forest leaf chlorophyll content, leaf mass per area, leaf area index and leaf canopy biomass , 2008 .
[114] Jason S. Lupoi,et al. Characterization of Woody and Herbaceous Biomasses Lignin Composition with 1064 nm Dispersive Multichannel Raman Spectroscopy , 2012, Applied spectroscopy.
[115] Kristian Kersting,et al. Hyperspectral phenotyping on the microscopic scale: towards automated characterization of plant-pathogen interactions , 2015, Plant Methods.
[116] Edward S Yeung,et al. Use of mass spectrometry for imaging metabolites in plants. , 2012, The Plant journal : for cell and molecular biology.
[117] A. Copetta,et al. Impact of two fluorescent pseudomonads and an arbuscular mycorrhizal fungus on tomato plant growth, root architecture and P acquisition , 2004, Mycorrhiza.
[118] Fumin Wang,et al. Quantifying biochemical variables of corn by hyperspectral reflectance at leaf scale , 2008, Journal of Zhejiang University SCIENCE B.
[119] S. Fukai,et al. Breeding Rice for Drought-Prone Environments , 2003 .
[120] M. Ashton,et al. Hyperion, IKONOS, ALI, and ETM+ sensors in the study of African rainforests , 2004 .
[121] 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.
[122] Martin Fregene,et al. Phenotypic approaches to drought in cassava: review , 2012, Front. Physiol..
[123] Fei Liu,et al. Hyperspectral Imaging for Mapping of Total Nitrogen Spatial Distribution in Pepper Plant , 2014, PloS one.
[124] T. Vuorinen,et al. Effect of pH on lignin analysis by Raman spectroscopy , 2012 .
[125] Mark F. Davis,et al. Evaluating Lignocellulosic Biomass, Its Derivatives, and Downstream Products with Raman Spectroscopy , 2015, Front. Bioeng. Biotechnol..
[126] L. Xiong,et al. Combining high-throughput phenotyping and genome-wide association studies to reveal natural genetic variation in rice , 2014, Nature Communications.
[127] Fabio Fiorani,et al. The art of growing plants for experimental purposes: a practical guide for the plant biologist. , 2012, Functional plant biology : FPB.
[128] Xavier Sirault,et al. A new screening method for osmotic component of salinity tolerance in cereals using infrared thermography. , 2009, Functional plant biology : FPB.
[129] Anne-Katrin Mahlein,et al. Recent advances in sensing plant diseases for precision crop protection , 2012, European Journal of Plant Pathology.
[130] K. Mogensen,et al. Recent developments in detection for microfluidic systems , 2004, Electrophoresis.
[131] V. Kakani,et al. Selection of Optimum Reflectance Ratios for Estimating Leaf Nitrogen and Chlorophyll Concentrations of Field-Grown Cotton , 2005 .
[132] C. Zipfel,et al. Plant PRRs and the activation of innate immune signaling. , 2014, Molecular cell.
[133] A. Gowen,et al. Prediction of polyphenol oxidase activity using visible near-infrared hyperspectral imaging on mushroom (Agaricus bisporus) caps. , 2010, Journal of agricultural and food chemistry.
[134] D. Bhatnagar,et al. Correlation and classification of single kernel fluorescence hyperspectral data with aflatoxin concentration in corn kernels inoculated with Aspergillus flavus spores , 2010, Food additives & contaminants. Part A, Chemistry, analysis, control, exposure & risk assessment.
[135] T. Almeida,et al. Principal component analysis applied to feature-oriented band ratios of hyperspectral data: A tool for vegetation studies , 2004 .
[136] A F Goetz,et al. Imaging Spectrometry for Earth Remote Sensing , 1985, Science.
[137] K. McNally,et al. Genomics of gene banks: A case study in rice. , 2012, American journal of botany.
[138] Robert T. Furbank,et al. Detection of decay in fresh-cut lettuce using hyperspectral imaging and chlorophyll fluorescence imaging , 2015 .
[139] Robert D. Hall,et al. Plant metabolomics strategies based upon quadrupole time of flight mass spectrometry (QTOF-MS) , 2006 .
[140] W. Weschke,et al. Spatio-Temporal Dynamics of Fructan Metabolism in Developing Barley Grains[W] , 2014, Plant Cell.
[141] Birger Eriksen,et al. Analysis of pregerminated barley using hyperspectral image analysis. , 2011, Journal of agricultural and food chemistry.
[142] Steve A. Kay,et al. Daily Changes in Temperature, Not the Circadian Clock, Regulate Growth Rate in Brachypodium distachyon , 2014, PloS one.
[143] Bodo Mistele,et al. High throughput phenotyping of canopy water mass and canopy temperature in well-watered and drought stressed tropical maize hybrids in the vegetative stage , 2011 .
[144] J. Araus,et al. Field high-throughput phenotyping: the new crop breeding frontier. , 2014, Trends in plant science.