Proximal Remote Sensing Buggies and Potential Applications for Field-Based Phenotyping

The achievements made in genomic technology in recent decades are yet to be matched by fast and accurate crop phenotyping methods. Such crop phenotyping methods are required for crop improvement efforts to meet expected demand for food and fibre in the future. This review evaluates the role of proximal remote sensing buggies for field-based phenotyping with a particular focus on the application of currently available sensor technology for large-scale field phenotyping. To illustrate the potential for the development of high throughput phenotyping techniques, a case study is presented with sample data sets obtained from a ground-based proximal remote sensing buggy mounted with the following sensors: LiDAR, RGB camera, thermal infra-red camera and imaging spectroradiometer. The development of such techniques for routine deployment in commercial-scale breeding and pre-breeding operations will require a multidisciplinary approach to leverage the recent technological advances realised in computer science, image analysis, proximal remote sensing and robotics.

[1]  A. Escolà,et al.  Ultrasonic and LIDAR Sensors for Electronic Canopy Characterization in Vineyards: Advances to Improve Pesticide Application Methods , 2011, Sensors.

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

[3]  Samsuzana Abd Aziz,et al.  Ultrasonic Sensing for Corn Plant Canopy Characterization , 2004 .

[4]  Weiping Yang,et al.  Original paper: Efficient registration of optical and IR images for automatic plant water stress assessment , 2010 .

[5]  Karen Anderson,et al.  Lightweight unmanned aerial vehicles will revolutionize spatial ecology , 2013 .

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

[7]  M. A. Jiménez-Bello,et al.  Development and validation of an automatic thermal imaging process for assessing plant water status , 2011 .

[8]  E. Hunt,et al.  Early season remote sensing of wheat nitrogen status using a green scanning laser , 2011 .

[9]  Jean-Philippe Gastellu-Etchegorry,et al.  DART: a 3D model for simulating satellite images and studying surface radiation budget , 2004 .

[10]  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.

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

[12]  Pär K Ingvarsson,et al.  Association genetics of complex traits in plants. , 2011, The New phytologist.

[13]  Z. Malenovský,et al.  Scientific and technical challenges in remote sensing of plant canopy reflectance and fluorescence. , 2009, Journal of experimental botany.

[14]  Evelyne Costes,et al.  Contribution of airborne remote sensing to high-throughput phenotyping of a hybrid apple population in response to soil water constraints , 2011 .

[15]  Alessandro Matese,et al.  DEVELOPMENT AND APPLICATION OF AN AUTONOMOUS AND FLEXIBLE UNMANNED AERIAL VEHICLE FOR PRECISION VITICULTURE , 2013 .

[16]  R. C. Muchow,et al.  Radiation Use Efficiency , 1999 .

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

[18]  I Leinonen,et al.  Estimating stomatal conductance with thermal imagery. , 2006, Plant, cell & environment.

[19]  John R. Miller,et al.  Imaging chlorophyll fluorescence with an airborne narrow-band multispectral camera for vegetation stress detection , 2009 .

[20]  Rachel Gaulton,et al.  The potential of dual-wavelength laser scanning for estimating vegetation moisture content , 2013 .

[21]  P. Zarco-Tejada,et al.  Modelling PRI for water stress detection using radiative transfer models , 2009 .

[22]  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 .

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

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

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

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

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

[28]  Karine Chenu,et al.  Plot size matters: interference from intergenotypic competition in plant phenotyping studies. , 2014, Functional plant biology : FPB.

[29]  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.

[30]  Óscar Pérez-Priego,et al.  Detection of water stress in orchard trees with a high-resolution spectrometer through chlorophyll fluorescence in-filling of the O/sub 2/-A band , 2005, IEEE Transactions on Geoscience and Remote Sensing.

[31]  Carina Moeller,et al.  A multisite managed environment facility for targeted trait and germplasm phenotyping. , 2012, Functional plant biology : FPB.

[32]  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 .

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

[34]  Jan U.H. Eitel,et al.  Disentangling the relationships between plant pigments and the photochemical reflectance index reveals a new approach for remote estimation of carotenoid content , 2011 .

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

[36]  P. Zarco-Tejada,et al.  Relationships between net photosynthesis and steady-state chlorophyll fluorescence retrieved from airborne hyperspectral imagery , 2013 .

[37]  Andrew Zisserman,et al.  Multiple View Geometry in Computer Vision (2nd ed) , 2003 .

[38]  H. Jones.  The use of indirect or proxy markers in plant physiology. , 2014, Plant, cell & environment.

[39]  M. A. Jiménez-Bello,et al.  Usefulness of thermography for plant water stress detection in citrus and persimmon trees , 2013 .

[40]  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 .

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

[42]  Changming Sun,et al.  Fast Stereo Matching Using Rectangular Subregioning and 3D Maximum-Surface Techniques , 2002, International Journal of Computer Vision.

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

[44]  J. Passioura,et al.  Grain yield, harvest index, and water use of wheat. , 1977 .

[45]  Manfred Stoll,et al.  Use of infrared thermography for monitoring stomatal closure in the field: application to grapevine. , 2002, Journal of experimental botany.

[46]  R. A. Fischer,et al.  Canopy Temperature Depression Association with Yield of Irrigated Spring Wheat Cultivars in a Hot Climate , 1996 .

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

[48]  Nicola Cooley,et al.  Automated canopy temperature estimation via infrared thermography: A first step towards automated plant water stress monitoring , 2010 .

[49]  A. Skidmore,et al.  Identifying plant species using mid-wave infrared (2.5–6 μm) and thermal infrared (8–14 μm) emissivity spectra , 2012 .

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

[51]  Matthew P. Reynolds,et al.  AT INTERNATIONAL WORKSHOP ON INCREASING WHEAT YIELD POTENTIAL , CIMMYT , OBREGON , MEXICO , 20 – 24 MARCH 2006 An economic assessment of the use of physiological selection for stomatal aperture-related traits in the CIMMYT wheat breeding programme , 2007 .

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

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

[54]  Graham D. Farquhar,et al.  Genomic regions for canopy temperature and their genetic association with stomatal conductance and grain yield in wheat. , 2012, Functional plant biology : FPB.

[55]  P. Zarco-Tejadaa,et al.  Estimating leaf carotenoid content in vineyards using high resolution hyperspectral imagery acquired from an unmanned aerial vehicle ( UAV ) , 2013 .

[56]  F. Baret,et al.  GAI estimates of row crops from downward looking digital photos taken perpendicular to rows at 57.5° zenith angle: Theoretical considerations based on 3D architecture models and application to wheat crops , 2010 .

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

[58]  Roberto Tuberosa,et al.  Translational research impacting on crop productivity in drought-prone environments. , 2008, Current opinion in plant biology.

[59]  D. Cozzolino,et al.  Non-destructive measurement of grapevine water potential using near infrared spectroscopy , 2011 .

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

[61]  J. Monteith.  Climate and the efficiency of crop production in Britain , 1977 .

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

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

[64]  K. Barry,et al.  Optimizing spectral indices and chemometric analysis of leaf chemical properties using radiative transfer modeling , 2011 .

[65]  Jeffrey W. White,et al.  A Flexible, Low‐Cost Cart for Proximal Sensing , 2013 .

[66]  John A. Harrington,et al.  On defining remote sensing , 1986 .

[67]  Yongjiang Zhang,et al.  Assessing photosynthetic light-use efficiency using a solar-induced chlorophyll fluorescence and photochemical reflectance index , 2013 .

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

[69]  P. Zarco-Tejada,et al.  Fluorescence, temperature and narrow-band indices acquired from a UAV platform for water stress detection using a micro-hyperspectral imager and a thermal camera , 2012 .

[70]  R. Tuberosa.  Phenotyping for drought tolerance of crops in the genomics era , 2012, Front. Physio..

[71]  Duane C. Brown,et al.  Close-Range Camera Calibration , 1971 .

[72]  Andrew Hall,et al.  Object-based analysis of grapevine canopy relationships with winegrape composition and yield in two contrasting vineyards using multitemporal high spatial resolution optical remote sensing , 2013 .

[73]  V. Demarez,et al.  Modeling radiative transfer in heterogeneous 3-D vegetation canopies , 1996 .

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

[75]  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.

[76]  Jose Crossa,et al.  Gene action of canopy temperature in bread wheat under diverse environments , 2009, Theoretical and Applied Genetics.

[77]  J. Llorens,et al.  Relationship between tree row LIDAR-volume and leaf area density for fruit orchards and vineyards obtained with a LIDAR 3D Dynamic Measurement System , 2013 .

[78]  Roberta E. Martin,et al.  Airborne spectranomics: mapping canopy chemical and taxonomic diversity in tropical forests , 2009 .

[79]  Claes Lund Dühring.  A Low Cost, Modular Robotics Tool Carrier For Precision Agriculture Research , 2016 .

[80]  D. Tilman,et al.  Global food demand and the sustainable intensification of agriculture , 2011, Proceedings of the National Academy of Sciences.

[81]  H. Vereecken,et al.  High-resolution imaging of a vineyard in south of France using ground penetrating radar, electromagnetic induction and electrical resistivity tomography , 2012 .

[82]  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.

[83]  H. Jones.  Application of Thermal Imaging and Infrared Sensing in Plant Physiology and Ecophysiology , 2004 .

[84]  W. Sutherland,et al.  Reaping the Benefits: Science and the sustainable intensification of global agriculture , 2009 .

[85]  Graham D. Farquhar,et al.  Using Stomatal Aperture-Related Traits to Select for High Yield Potential in Bread Wheat , 2007 .

[86]  C. Field,et al.  A narrow-waveband spectral index that tracks diurnal changes in photosynthetic efficiency , 1992 .

[87]  John R. Miller,et al.  Assessing vineyard condition with hyperspectral indices: Leaf and canopy reflectance simulation in a row-structured discontinuous canopy , 2005 .

[88]  James W. McNicol,et al.  Infra-red Thermography for High Throughput Field Phenotyping in Solanum tuberosum , 2013, PLoS ONE.

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

[90]  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 .

[91]  Qin Zhang,et al.  Shadow effect on multi-spectral image for detection of nitrogen deficiency in corn , 2012 .

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

[93]  Yong Hu,et al.  A Novel in Situ FPAR Measurement Method for Low Canopy Vegetation Based on a Digital Camera and Reference Panel , 2013, Remote. Sens..

[94]  Luis Alonso,et al.  A METHOD FOR THE DETECTION OF SOLAR-INDUCED VEGETATION FLUOR ESCENCE FROM MERIS FR DATA , 2007 .

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

[96]  Uwe Rascher,et al.  Measuring photosynthetic parameters at a distance: laser induced fluorescence transient (LIFT) method for remote measurements of photosynthesis in terrestrial vegetation , 2005, Photosynthesis Research.

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

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

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

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

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

[102]  Bernhard P. Wrobel,et al.  Multiple View Geometry in Computer Vision , 2001 .

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

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

[105]  Guangjian Yan,et al.  Image-based 3D corn reconstruction for retrieval of geometrical structural parameters , 2009 .

[106]  M. P. Reynolds,et al.  Physiological breeding II: a field guide to wheat phenotyping , 2012 .

[107]  D. C. Brown,et al.  Lens distortion for close-range photogrammetry , 1986 .

[108]  Emanuele Trucco,et al.  Introductory techniques for 3-D computer vision , 1998 .

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

[110]  Thomas Hilker,et al.  Detection of foliage conditions and disturbance from multi-angular high spectral resolution remote sensing , 2009 .