Evaluating maize phenotype dynamics under drought stress using terrestrial lidar

[1]  I. C. Prentice,et al.  How should we model plant responses to drought? An analysis of stomatal and non-stomatal responses to water stress , 2013 .

[2]  Qin Zhang,et al.  A Stereovision-based Crop Row Detection Method for Tractor-automated Guidance , 2005 .

[3]  John R. Miller,et al.  Integrated narrow-band vegetation indices for prediction of crop chlorophyll content for application to precision agriculture , 2002 .

[4]  George E. Meyer,et al.  An Electronic Image Plant Growth Measurement System , 1987 .

[5]  D. Turner,et al.  Photosynthetic and growth responses of juvenile Chinese kale (Brassica oleracea var. alboglabra) and Caisin (Brassica rapa subsp. parachinensis) to waterlogging and water deficit , 2007 .

[6]  S. Chapman,et al.  Dynamic quantification of canopy structure to characterize early plant vigour in wheat genotypes , 2016, Journal of experimental botany.

[7]  M. Farooq,et al.  Broader leaves result in better performance of indica rice under drought stress. , 2010, Journal of plant physiology.

[8]  M. Fetene,et al.  Growth, Water Status, Physiological, Biochemical and Yield Response of Stay Green Sorghum (Sorghum bicolor (L.) Moench) Varieties-A Field Trial Under Drought-Prone Area in Amhara Regional State, Ethiopia , 2015 .

[9]  Yanjun Su,et al.  Improved progressive TIN densification filtering algorithm for airborne LiDAR data in forested areas , 2016 .

[10]  P. Krajewski,et al.  Genetic analysis of drought tolerance in maize by molecular markers. II. Plant height and flowering , 1999, Theoretical and Applied Genetics.

[11]  H. S. Jacobs,et al.  Water‐Use Efficiency and Its Relation to Crop Canopy Area, Stomatal Regulation, and Root Distribution1 , 1973 .

[12]  Guillaume Lobet,et al.  Image Analysis in Plant Sciences: Publish Then Perish. , 2017, Trends in plant science.

[13]  Xinyou Yin,et al.  Role of crop physiology in predicting gene-to-phenotype relationships. , 2004, Trends in plant science.

[14]  John F. Reid,et al.  Stereo vision three-dimensional terrain maps for precision agriculture , 2008 .

[15]  Yanjun Su,et al.  A practical method for SRTM DEM correction over vegetated mountain areas , 2014 .

[16]  F. R. Bidinger,et al.  Assessment of drought resistance in pearl millet ( Pennisetum americanum (L.) Leeke). II. Estimation of genotype response to stress , 1987 .

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

[18]  Hanno Scharr,et al.  Image Analysis: The New Bottleneck in Plant Phenotyping [Applications Corner] , 2015, IEEE Signal Processing Magazine.

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

[20]  Kenji Omasa,et al.  3-D Modeling of Tomato Canopies Using a High-Resolution Portable Scanning Lidar for Extracting Structural Information , 2011, Sensors.

[21]  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..

[22]  Mark E. Cooper,et al.  Improving drought tolerance in maize: a view from industry , 2004 .

[23]  T. Sinclair,et al.  Physiological phenotyping of plants for crop improvement. , 2015, Trends in plant science.

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

[25]  Stephen M. Welch,et al.  Plant high-throughput phenotyping using photogrammetry and imaging techniques to measure leaf length and rosette area , 2016, Comput. Electron. Agric..

[26]  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.

[27]  H. Herzog,et al.  Water-use efficiency, leaf area and leaf gas exchange of cowpeas under mid-season drought , 2004 .

[28]  T. Mockler,et al.  High throughput phenotyping to accelerate crop breeding and monitoring of diseases in the field. , 2017, Current opinion in plant biology.

[29]  Xiaodong Yan,et al.  Climate change and drought: a risk assessment of crop-yield impacts. , 2009 .

[30]  Lei Zhang,et al.  A LIDAR-based crop height measurement system for Miscanthus giganteus , 2012 .

[31]  J. Levitt,et al.  Responses of Plants to Environmental Stress, 2nd Edition, Volume 1: Chilling, Freezing, and High Temperature Stresses. , 1980 .

[32]  F. Baret,et al.  Review of methods for in situ leaf area index (LAI) determination: Part II. Estimation of LAI, errors and sampling , 2004 .

[33]  J. Schjoerring,et al.  Reflectance measurement of canopy biomass and nitrogen status in wheat crops using normalized difference vegetation indices and partial least squares regression , 2003 .

[34]  Jin Liu,et al.  Stem–Leaf Segmentation and Phenotypic Trait Extraction of Individual Maize Using Terrestrial LiDAR Data , 2019, IEEE Transactions on Geoscience and Remote Sensing.

[35]  Hideyuki Shimizu,et al.  Plant responses to drought and rewatering , 2010, Plant signaling & behavior.

[36]  D. Mulla.  Twenty five years of remote sensing in precision agriculture: Key advances and remaining knowledge gaps , 2013 .

[37]  Q. Guo,et al.  Crop 3D—a LiDAR based platform for 3D high-throughput crop phenotyping , 2018, Science China Life Sciences.

[38]  M. Chatzidimopoulos,et al.  Detection and characterization of fungicide resistant phenotypes of Botrytis cinerea in lettuce crops in Greece , 2013, European Journal of Plant Pathology.

[39]  B. Miflin,et al.  Crop improvement in the 21st century. , 2000, Journal of experimental botany.

[40]  Laura Chasmer,et al.  Investigating laser pulse penetration through a conifer canopy by integrating airborne and terrestrial lidar , 2006 .

[41]  Yufeng Ge,et al.  A Novel LiDAR-Based Instrument for High-Throughput, 3D Measurement of Morphological Traits in Maize and Sorghum , 2018, Sensors.

[42]  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 .

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

[44]  M. Farooq,et al.  Plant drought stress: effects, mechanisms and management , 2011, Agronomy for Sustainable Development.

[45]  L. S. Pereira,et al.  Assessing the performance of the FAO AquaCrop model to estimate maize yields and water use under full and deficit irrigation with focus on model parameterization , 2014 .

[46]  Yi Lin,et al.  LiDAR: An important tool for next-generation phenotyping technology of high potential for plant phenomics? , 2015, Comput. Electron. Agric..

[47]  C. Klukas,et al.  Advanced phenotyping and phenotype data analysis for the study of plant growth and development , 2015, Front. Plant Sci..

[48]  G. Bareth,et al.  EVALUATING THE POTENTIAL OF CONSUMER-GRADE SMART CAMERAS FOR LOW-COST STEREO-PHOTOGRAMMETRIC CROP-SURFACE MONITORING , 2014 .

[49]  Roberto Tuberosa,et al.  Genomics-based approaches to improve drought tolerance of crops. , 2006, Trends in plant science.

[50]  J. Léon,et al.  High-precision laser scanning system for capturing 3D plant architecture and analysing growth of cereal plants , 2014 .

[51]  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.

[52]  R. Fischer,et al.  Drought resistance in spring wheat cultivars, 1. Grain yield responses. , 1978 .

[53]  How to Feed the World in 2050 , 2009 .

[54]  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.

[55]  Ghasem Hosseini Salekdeh,et al.  Conceptual framework for drought phenotyping during molecular breeding. , 2009, Trends in plant science.

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

[57]  Douglas E. Karcher,et al.  Quantifying Turfgrass Cover Using Digital Image Analysis , 2001 .

[58]  José Dorado,et al.  Application note: Potential of a terrestrial LiDAR-based system to characterise weed vegetation in maize crops , 2013 .

[59]  S. Chapman,et al.  Selection Improves Drought Tolerance in Tropical Maize Populations: II. Direct and Correlated Responses among Secondary Traits , 1999 .

[60]  A. Dai.  Increasing drought under global warming in observations and models , 2013 .

[61]  Fusheng Li,et al.  Multi-scale evapotranspiration of summer maize and the controlling meteorological factors in north China , 2016 .

[62]  D. Lawlor,et al.  The effects of drought on barley growth: models and measurements showing the relative importance of leaf area and photosynthetic rate , 1979, The Journal of Agricultural Science.

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

[64]  Alexandre Escolà,et al.  Discriminating Crop, Weeds and Soil Surface with a Terrestrial LIDAR Sensor , 2013, Sensors.

[65]  J. Edwards.  Maize growth and development. , 2009 .

[66]  Jean-Marcel Ribaut,et al.  Drought phenotyping in crops: From theory to practice. , 2014 .

[67]  Q. Guo,et al.  Effects of Topographic Variability and Lidar Sampling Density on Several DEM Interpolation Methods , 2010 .

[68]  J. Ribaut,et al.  Quantitative trait loci for yield and correlated traits under high and low soil nitrogen conditions in tropical maize , 2007, Molecular Breeding.

[69]  K. Poustini,et al.  Evaluation of drought resistance indices under various environmental conditions , 2006 .

[70]  Daniel J. Aneshansley,et al.  2 3-Dimensional Root Phenotyping with a Novel Imaging and Software Platform , 2011 .