Plant Phenomics: Fundamental Bases, Software and Hardware Platforms, and Machine Learning

[1]  B. Rittmann,et al.  Growth kinetics and mathematical modeling of Synechocystis sp. PCC 6803 under flashing light. , 2018, Biotechnology and bioengineering.

[2]  Peter W. B. Phillips,et al.  The adoption of automated phenotyping by plant breeders , 2018, Euphytica.

[3]  M. Diago,et al.  On-The-Go Hyperspectral Imaging Under Field Conditions and Machine Learning for the Classification of Grapevine Varieties , 2018, Front. Plant Sci..

[4]  Y. Ge,et al.  Field-Based Scoring of Soybean Iron Deficiency Chlorosis Using RGB Imaging and Statistical Learning , 2018, Front. Plant Sci..

[5]  J. R. Rosell-Polo,et al.  Application of light detection and ranging and ultrasonic sensors to high-throughput phenotyping and precision horticulture: current status and challenges , 2018, Horticulture Research.

[6]  Stefano Bianchi,et al.  Seed-per-pod estimation for plant breeding using deep learning , 2018, Comput. Electron. Agric..

[7]  Shang Gao,et al.  Deep Learning: Individual Maize Segmentation From Terrestrial Lidar Data Using Faster R-CNN and Regional Growth Algorithms , 2018, Front. Plant Sci..

[8]  D. Sandhu,et al.  Genetics and Physiology of the Nuclearly Inherited Yellow Foliar Mutants in Soybean , 2018, Front. Plant Sci..

[9]  U. Güldener,et al.  The ‘PhenoBox’, a flexible, automated, open‐source plant phenotyping solution , 2018, The New phytologist.

[10]  Daniela Ewe,et al.  High light acclimation of Chromera velia points to photoprotective NPQ , 2017, Photosynthesis Research.

[11]  Yu Jiang,et al.  Aerial Images and Convolutional Neural Network for Cotton Bloom Detection , 2018, Front. Plant Sci..

[12]  Jordan R. Ubbens,et al.  The use of plant models in deep learning: an application to leaf counting in rosette plants , 2018, Plant Methods.

[13]  A. Frolov,et al.  Photochemical activity changes accompanying the embryogenesis of pea (Pisum sativum) with yellow and green cotyledons. , 2018, Functional plant biology : FPB.

[14]  Rodomiro Ortiz,et al.  Editorial: Plant Phenotyping and Phenomics for Plant Breeding , 2017, Front. Plant Sci..

[15]  Andy Lin,et al.  PlantCV v2: Image analysis software for high-throughput plant phenotyping , 2017, PeerJ.

[16]  L. Xiong,et al.  Panicle-SEG: a robust image segmentation method for rice panicles in the field based on deep learning and superpixel optimization , 2017, Plant Methods.

[17]  Uwe Scholz,et al.  From plant genomes to phenotypes. , 2017, Journal of biotechnology.

[18]  Nathan Hughes,et al.  Non-destructive, high-content analysis of wheat grain traits using X-ray micro computed tomography , 2017, Plant Methods.

[19]  Pedro J. Navarro,et al.  Plant phenomics: an overview of image acquisition technologies and image data analysis algorithms , 2017, GigaScience.

[20]  Antoine Harfouche,et al.  UAV-Based Thermal Imaging for High-Throughput Field Phenotyping of Black Poplar Response to Drought , 2017, Front. Plant Sci..

[21]  J. Yosinski,et al.  Automated Identification of Northern Leaf Blight-Infected Maize Plants from Field Imagery Using Deep Learning. , 2017, Phytopathology.

[22]  Maria A. de Luis Balaguer,et al.  Genetic Architecture and Molecular Networks Underlying Leaf Thickness in Desert-Adapted Tomato Solanum pennellii1[OPEN] , 2017, Plant Physiology.

[23]  X. C. Wang,et al.  Constitutive down-regulation of SiSGR gene is related to green millet in Setaria italica , 2017, Russian journal of plant physiology.

[24]  Roland Gerhards,et al.  Utilization of Chlorophyll Fluorescence Imaging Technology to Detect Plant Injury by Herbicides in Sugar Beet and Soybean , 2017, Weed Technology.

[25]  L. Gratani,et al.  Highlighting the differential role of leaf paraheliotropism in two Mediterranean Cistus species under drought stress and well-watered conditions. , 2017, Journal of plant physiology.

[26]  J. Borevitz,et al.  Deep phenotyping: deep learning for temporal phenotype/genotype classification , 2017, bioRxiv.

[27]  Patrik R. Jones,et al.  Pyridine nucleotide transhydrogenase PntAB is essential for optimal growth and photosynthetic integrity under low-light mixotrophic conditions in Synechocystis sp. PCC 6803. , 2017, The New phytologist.

[28]  Baskar Ganapathysubramanian,et al.  Computer vision and machine learning for robust phenotyping in genome-wide studies , 2017, Scientific Reports.

[29]  Ulrich Schurr,et al.  Field Phenotyping: Concepts and Examples to Quantify Dynamic Plant Traits across Scales in the Field , 2017 .

[30]  D. Van Der Straeten,et al.  Differential coupling of gibberellin responses by Rht-B1c suppressor alleles and Rht-B1b in wheat highlights a unique role for the DELLA N-terminus in dormancy , 2017, Journal of experimental botany.

[31]  Ring T. Cardé,et al.  Simulation Modeling to Interpret the Captures of Moths in Pheromone-Baited Traps Used for Surveillance of Invasive Species: the Gypsy Moth as a Model Case , 2016, Journal of Chemical Ecology.

[32]  D. Afonnikov,et al.  Methods of high-throughput plant phenotyping for large-scale breeding and genetic experiments , 2016, Russian Journal of Genetics.

[33]  Rogério Schmidt Feris,et al.  A Unified Multi-scale Deep Convolutional Neural Network for Fast Object Detection , 2016, ECCV.

[34]  M. Suorsa,et al.  Light acclimation in the lycophyte Selaginella martensii depends on changes in the amount of photosystems and on the flexibility of the light-harvesting complex II antenna association with both photosystems. , 2016, The New phytologist.

[35]  Mark G. M. Aarts,et al.  Phenomics for photosynthesis, growth and reflectance in Arabidopsis thaliana reveals circadian and long-term fluctuations in heritability , 2016, Plant Methods.

[36]  I. König,et al.  Machine learning and data mining in complex genomic data—a review on the lessons learned in Genetic Analysis Workshop 19 , 2016, BMC Genetics.

[37]  C. Webber,et al.  Systematic Phenomics Analysis Deconvolutes Genes Mutated in Intellectual Disability into Biologically Coherent Modules. , 2016, American journal of human genetics.

[38]  C. Fournier,et al.  High-throughput estimation of incident light, light interception and radiation-use efficiency of thousands of plants in a phenotyping platform. , 2015, The New phytologist.

[39]  J. Weitz,et al.  Digital imaging of root traits (DIRT): a high-throughput computing and collaboration platform for field-based root phenomics , 2015, Plant Methods.

[40]  Junya Chen,et al.  Non-invasive, whole-plant imaging of chloroplast movement and chlorophyll fluorescence reveals photosynthetic phenotypes independent of chloroplast photorelocation defects in chloroplast division mutants. , 2015, The Plant journal : for cell and molecular biology.

[41]  Pedro Andrade-Sanchez,et al.  Proximal hyperspectral sensing and data analysis approaches for field-based plant phenomics , 2015, Comput. Electron. Agric..

[42]  Hamid Laga,et al.  RootAnalyzer: A Cross-Section Image Analysis Tool for Automated Characterization of Root Cells and Tissues , 2015, PloS one.

[43]  María-Paz Diago,et al.  vitisFlower®: Development and Testing of a Novel Android-Smartphone Application for Assessing the Number of Grapevine Flowers per Inflorescence Using Artificial Vision Techniques , 2015, Sensors.

[44]  Evelyne Costes,et al.  Multispectral airborne imagery in the field reveals genetic determinisms of morphological and transpiration traits of an apple tree hybrid population in response to water deficit , 2015, Journal of experimental botany.

[45]  Dirk Inzé,et al.  A Journey Through a Leaf: Phenomics Analysis of Leaf Growth in Arabidopsis thaliana , 2015, The arabidopsis book.

[46]  Volker Steinhage,et al.  Automated 3D reconstruction of grape cluster architecture from sensor data for efficient phenotyping , 2015, Comput. Electron. Agric..

[47]  Arnaud Martin,et al.  The differential view of genotype–phenotype relationships , 2015, Front. Genet..

[48]  J. Flexas,et al.  UAVs challenge to assess water stress for sustainable agriculture , 2015 .

[49]  T. Sharkey,et al.  The relationship between leaf area growth and biomass accumulation in Arabidopsis thaliana , 2015, Front. Plant Sci..

[50]  B. van Duijn,et al.  Can chlorophyll fluorescence be used to determine the optimal time to harvest rice seeds for long-term genebank storage? , 2015, Seed Science Research.

[51]  A. Gandomi,et al.  Beyond the hype: Big data concepts, methods, and analytics , 2015, Int. J. Inf. Manag..

[52]  Malia A. Gehan,et al.  Lights, camera, action: high-throughput plant phenotyping is ready for a close-up. , 2015, Current opinion in plant biology.

[53]  G. Bassel,et al.  Digital Single-Cell Analysis of Plant Organ Development Using 3DCellAtlas[OPEN] , 2015, Plant Cell.

[54]  Jan F. Humplík,et al.  Automated integrative high-throughput phenotyping of plant shoots: a case study of the cold-tolerance of pea (Pisum sativum L.) , 2015, Plant Methods.

[55]  Gil Alterovitz,et al.  Seeing the forest through the trees: uncovering phenomic complexity through interactive network visualization , 2015, J. Am. Medical Informatics Assoc..

[56]  Aboul Ella Hassanien,et al.  Using machine learning techniques for evaluating tomato ripeness , 2015, Expert Syst. Appl..

[57]  D. Inzé,et al.  Leaf Responses to Mild Drought Stress in Natural Variants of Arabidopsis1[OPEN] , 2015, Plant Physiology.

[58]  Laura M. Jackson,et al.  Finding Our Way through Phenotypes , 2015, PLoS biology.

[59]  A. Heyer,et al.  Chlorophyll fluorescence emission can screen cold tolerance of cold acclimated Arabidopsis thaliana accessions , 2014, Plant Methods.

[60]  Qin Zhang,et al.  A Review of Imaging Techniques for Plant Phenotyping , 2014, Sensors.

[61]  Masahide Kikkawa,et al.  High-throughput phenotyping of chlamydomonas swimming mutants based on nanoscale video analysis. , 2014, Biophysical journal.

[62]  M. Plazas,et al.  Conventional and phenomics characterization provides insight into the diversity and relationships of hypervariable scarlet (Solanum aethiopicum L.) and gboma (S. macrocarpon L.) eggplant complexes , 2014, Front. Plant Sci..

[63]  C. Granier,et al.  Phenotyping and beyond: modelling the relationships between traits. , 2014, Current opinion in plant biology.

[64]  Christoph Sommer,et al.  Machine learning in cell biology – teaching computers to recognize phenotypes , 2013, Journal of Cell Science.

[65]  C. Foyer,et al.  A phenomics approach to the analysis of the influence of glutathione on leaf area and abiotic stress tolerance in Arabidopsis thaliana , 2013, Front. Plant Sci..

[66]  Jianhua Zhu,et al.  A Nuclear Calcium-Sensing Pathway Is Critical for Gene Regulation and Salt Stress Tolerance in Arabidopsis , 2013, PLoS genetics.

[67]  Thomas G. Dietterich,et al.  Next-generation phenomics for the Tree of Life , 2013, PLoS currents.

[68]  Michael P. Pound,et al.  RootNav: Navigating Images of Complex Root Architectures1[C][W] , 2013, Plant Physiology.

[69]  William Stafford Noble,et al.  Integrative phenomics reveals insight into the structure of phenotypic diversity in budding yeast , 2013, Genome research.

[70]  Kevin Y. Yip,et al.  Machine learning and genome annotation: a match meant to be? , 2013, Genome Biology.

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

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

[73]  M. Tsai,et al.  Demonstration of Lateral IGBTs in 4H-SiC , 2013, IEEE Electron Device Letters.

[74]  Guo-ping Zhang,et al.  Seed Fatty Acid Reducer acts downstream of gibberellin signalling pathway to lower seed fatty acid storage in Arabidopsis. , 2012, Plant, cell & environment.

[75]  Gustavo A. Pereyra-Irujo,et al.  GlyPh: a low-cost platform for phenotyping plant growth and water use. , 2012, Functional plant biology : FPB.

[76]  M. Yano,et al.  SmartGrain: High-Throughput Phenotyping Software for Measuring Seed Shape through Image Analysis1[C][W][OA] , 2012, Plant Physiology.

[77]  Brad T. Moore,et al.  GiA Roots: software for the high throughput analysis of plant root system architecture , 2012, BMC Plant Biology.

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

[79]  Samuel David Lee,et al.  Financing from Family and Friends , 2012 .

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

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

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

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

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

[85]  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.

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

[87]  G. Ya. Wiederschain,et al.  Data mining techniques for the life sciences , 2011, Biochemistry (Moscow).

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

[89]  James C. Schnable,et al.  Genes Identified by Visible Mutant Phenotypes Show Increased Bias toward One of Two Subgenomes of Maize , 2011, PloS one.

[90]  C. Tonelli,et al.  Survival and growth of Arabidopsis plants given limited water are not equal , 2011, Nature Biotechnology.

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

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

[93]  Christos Davatzikos,et al.  Classification of brain tumor type and grade using MRI texture and shape in a machine learning scheme , 2009, Magnetic resonance in medicine.

[94]  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.

[95]  Tobias Wojciechowski,et al.  Root phenomics of crops: opportunities and challenges. , 2009, Functional plant biology : FPB.

[96]  Yann LeCun,et al.  Comparing SVM and convolutional networks for epileptic seizure prediction from intracranial EEG , 2008, 2008 IEEE Workshop on Machine Learning for Signal Processing.

[97]  Daniel Cremers,et al.  Integral Invariants for Shape Matching , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[98]  R. Gerlai.  Phenomics: fiction or the future? , 2002, Trends in Neurosciences.

[99]  Jason Weston,et al.  Gene Selection for Cancer Classification using Support Vector Machines , 2002, Machine Learning.

[100]  Brian McKenna,et al.  Beyond the Hype , 1998, Online Inf. Rev..

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