UAV-based high-throughput phenotyping to discriminate barley vigour with visible and near-infrared vegetation indices

ABSTRACT In the context of plant breeding, high-throughput phenotyping is an assessment of plant phenotypes on a scale and with a level of speed and precision not achievable with traditional methods, through the application of emerging technologies such as automation and robotics, new sensors, and imaging technologies (hardware and software). In the present work, high-resolution digital images have been acquired with an unmanned aerial vehicle (UAV) prototype platform on an experimental phenotyping barley field. Six vegetation indices generated from the red–green–blue and near-infrared-based images were calculated for 912 experimental barley plots and provided high correlation with the indices determined from hyperspectral data taken at the ground (gt); the indices performance in discriminating the vigour of genotypes was finally assessed.

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

[2]  T. Kraska,et al.  Phenological analysis of unmanned aerial vehicle based time series of barley imagery with high temporal resolution , 2018, Precision Agriculture.

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

[4]  F. López-Granados,et al.  Configuration and Specifications of an Unmanned Aerial Vehicle (UAV) for Early Site Specific Weed Management , 2013, PloS one.

[5]  Jose A. Jiménez-Berni,et al.  Methodology for High-Throughput Field Phenotyping of Canopy Temperature Using Airborne Thermography , 2016, Front. Plant Sci..

[6]  A. Gitelson,et al.  Use of a green channel in remote sensing of global vegetation from EOS- MODIS , 1996 .

[7]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[8]  J. E. Rasmussen,et al.  Potential uses of small unmanned aircraft systems (UAS) in weed research , 2013 .

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

[10]  Maria C. Garcia-Alegre,et al.  Development of an image analysis system for estimation of weed pressure. , 2005 .

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

[12]  G. Menexes,et al.  Assessment of Vegetation Indices Derived by UAV Imagery for Durum Wheat Phenotyping under a Water Limited and Heat Stressed Mediterranean Environment , 2017, Front. Plant Sci..

[13]  M. Quint,et al.  Ambient temperature and genotype differentially affect developmental and phenotypic plasticity in Arabidopsis thaliana , 2015, BMC Plant Biology.

[14]  P. Sellers.  Canopy reflectance, photosynthesis, and transpiration. II. the role of biophysics in the linearity of their interdependence , 1987 .

[15]  Kent M. Eskridge,et al.  Distinguishing between yield advances and yield plateaus in historical crop production trends , 2013, Nature Communications.

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

[17]  Sebastian Kipp,et al.  High-throughput phenotyping early plant vigour of winter wheat , 2014 .

[18]  P. Zarco-Tejada,et al.  Unmanned aerial platform-based multi-spectral imaging for field phenotyping of maize , 2015, Plant Methods.

[19]  Weixing Cao,et al.  Predicting grain yield in rice using multi-temporal vegetation indices from UAV-based multispectral and digital imagery , 2017 .

[20]  D. Goodin,et al.  Application of unmanned aerial systems for high throughput phenotyping of large wheat breeding nurseries , 2016, Plant Methods.

[21]  R. Wilke,et al.  Genotype-environment interactions and their translational implications. , 2011, Personalized medicine.

[22]  P. Sellers.  Canopy reflectance, photosynthesis and transpiration , 1985 .

[23]  Achim Walter,et al.  Remote, aerial phenotyping of maize traits with a mobile multi-sensor approach , 2015, Plant Methods.

[24]  T. Kataoka,et al.  Crop growth estimation system using machine vision , 2003, Proceedings 2003 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM 2003).

[25]  W. Raun,et al.  Potential Use of Spectral Reflectance Indices as a Selection Tool for Grain Yield in Winter Wheat under Great Plains Conditions , 2007 .

[26]  A. Formaggio,et al.  Influence of data acquisition geometry on soybean spectral response simulated by the prosail model , 2013 .

[27]  Katja Brinkmann,et al.  Monitoring of crop biomass using true colour aerial photographs taken from a remote controlled hexacopter , 2015 .

[28]  Achim Walter,et al.  The ETH field phenotyping platform FIP: a cable-suspended multi-sensor system. , 2016, Functional plant biology : FPB.

[29]  Michael E. Schaepman,et al.  Retrieval of foliar information about plant pigment systems from high resolution spectroscopy , 2009 .

[30]  J. A. Schell,et al.  Monitoring vegetation systems in the great plains with ERTS , 1973 .

[31]  Zilong Ma,et al.  RNA-Seq Analysis of Oil Palm under Cold Stress Reveals a Different C-Repeat Binding Factor (CBF) Mediated Gene Expression Pattern in Elaeis guineensis Compared to Other Species , 2014, PloS one.

[32]  Yuhong He,et al.  Mapping vegetation biophysical and biochemical properties using unmanned aerial vehicles-acquired imagery , 2018 .

[33]  C. Tucker.  Red and photographic infrared linear combinations for monitoring vegetation , 1979 .

[34]  Jon Nielsen,et al.  Are vegetation indices derived from consumer-grade cameras mounted on UAVs sufficiently reliable for assessing experimental plots? , 2016 .

[35]  Jörg Peter Baresel,et al.  Use of a digital camera as alternative method for non-destructive detection of the leaf chlorophyll content and the nitrogen nutrition status in wheat , 2017, Comput. Electron. Agric..

[36]  Martin J. Wooster,et al.  High Throughput Field Phenotyping of Wheat Plant Height and Growth Rate in Field Plot Trials Using UAV Based Remote Sensing , 2016, Remote. Sens..

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

[38]  M. Molnár-Láng,et al.  Wheat–barley hybridization: the last 40 years , 2014, Euphytica.

[39]  Charlie Walker,et al.  Estimating the nitrogen status of crops using a digital camera , 2010 .

[40]  William R. Raun,et al.  Estimating vegetation coverage in wheat using digital images , 1999 .

[41]  Wolfram Spreer,et al.  Use of thermography for high throughput phenotyping of tropical maize adaptation in water stress , 2011 .

[42]  S. Chapman,et al.  A Direct Comparison of Remote Sensing Approaches for High-Throughput Phenotyping in Plant Breeding , 2016, Front. Plant Sci..

[43]  C. Daughtry,et al.  Evaluation of Digital Photography from Model Aircraft for Remote Sensing of Crop Biomass and Nitrogen Status , 2005, Precision Agriculture.

[44]  William T B Thomas,et al.  Barley: a translational model for adaptation to climate change. , 2015, The New phytologist.

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

[46]  Andrea Berton,et al.  Assessment of a canopy height model (CHM) in a vineyard using UAV-based multispectral imaging , 2017 .

[47]  Piero Toscano,et al.  Intercomparison of UAV, Aircraft and Satellite Remote Sensing Platforms for Precision Viticulture , 2015, Remote. Sens..

[48]  Josep Peñuelas,et al.  The photochemical reflectance index (PRI) and the remote sensing of leaf, canopy and ecosystem radiation use efficiencies: A review and meta-analysis , 2011 .

[49]  David Jones,et al.  Individual leaf extractions from young canopy images using Gustafson-Kessel clustering and a genetic algorithm , 2006 .

[50]  Jose A. Jiménez-Berni,et al.  Pheno-Copter: A Low-Altitude, Autonomous Remote-Sensing Robotic Helicopter for High-Throughput Field-Based Phenotyping , 2014 .

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

[52]  D. Inzé,et al.  Cell to whole-plant phenotyping: the best is yet to come. , 2013, Trends in plant science.

[53]  Simon Bennertz,et al.  Combining UAV-based plant height from crop surface models, visible, and near infrared vegetation indices for biomass monitoring in barley , 2015, Int. J. Appl. Earth Obs. Geoinformation.

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

[55]  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 .