What is cost-efficient phenotyping? Optimizing costs for different scenarios.

[1]  F. Tardieu,et al.  The Physiological Basis of Drought Tolerance in Crop Plants: A Scenario-Dependent Probabilistic Approach. , 2018, Annual review of plant biology.

[2]  Suchismita Mondal,et al.  Combining High‐Throughput Phenotyping and Genomic Information to Increase Prediction and Selection Accuracy in Wheat Breeding , 2018, The plant genome.

[3]  Seth C. Murray,et al.  Multitemporal field-based plant height estimation using 3D point clouds generated from small unmanned aerial systems high-resolution imagery , 2018, Int. J. Appl. Earth Obs. Geoinformation.

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

[5]  Adrian Gracia-Romero,et al.  Comparative Performance of Ground vs. Aerially Assessed RGB and Multispectral Indices for Early-Growth Evaluation of Maize Performance under Phosphorus Fertilization , 2017, Front. Plant Sci..

[6]  Tony P. Pridmore,et al.  Deep Learning for Multi-task Plant Phenotyping , 2017, bioRxiv.

[7]  D. Dutartre,et al.  Leaf rolling in maize crops: from leaf scoring to canopy level measurements for phenotyping , 2017, bioRxiv.

[8]  Lars Grimstad,et al.  The Thorvald II Agricultural Robotic System , 2017, Robotics.

[9]  F. Baret,et al.  Estimates of plant density of wheat crops at emergence from very low altitude UAV imagery. , 2017 .

[10]  Christian Fournier,et al.  Distinct controls of leaf widening and elongation by light and evaporative demand in maize. , 2017, Plant, cell & environment.

[11]  Simon Griffiths,et al.  Leaf-GP: an open and automated software application for measuring growth phenotypes for arabidopsis and wheat , 2017, Plant Methods.

[12]  T. Pridmore,et al.  Plant Phenomics, From Sensors to Knowledge , 2017, Current Biology.

[13]  Javier Bajo,et al.  Combining Multi-Agent Systems and Wireless Sensor Networks for Monitoring Crop Irrigation , 2017, Sensors.

[14]  Simon Griffiths,et al.  CropQuant : An automated and scalable field phenotyping platform for crop 1 monitoring and trait measurements to facilitate breeding and digital agriculture , 2017 .

[15]  Wei Guo,et al.  High-Throughput Phenotyping of Sorghum Plant Height Using an Unmanned Aerial Vehicle and Its Application to Genomic Prediction Modeling , 2017, Front. Plant Sci..

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

[17]  Patrick Valduriez,et al.  InfraPhenoGrid: A scientific workflow infrastructure for plant phenomics on the Grid , 2017, Future Gener. Comput. Syst..

[18]  Mikhail Genaev,et al.  Evaluation of the SeedCounter, A Mobile Application for Grain Phenotyping , 2017, Front. Plant Sci..

[19]  Cathy Hawes,et al.  PHYLIS: A Low-Cost Portable Visible Range Spectrometer for Soil and Plants , 2017, Sensors.

[20]  Guilherme N. DeSouza,et al.  Vinobot and Vinoculer: Two Robotic Platforms for High-Throughput Field Phenotyping , 2017, Sensors.

[21]  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..

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

[23]  Uwe Scholz,et al.  Measures for interoperability of phenotypic data: minimum information requirements and formatting , 2016, Plant Methods.

[24]  David M. Kramer,et al.  MultispeQ Beta: a tool for large-scale plant phenotyping connected to the open PhotosynQ network , 2016, Royal Society Open Science.

[25]  Z. Niu,et al.  Remote estimation of canopy height and aboveground biomass of maize using high-resolution stereo images from a low-cost unmanned aerial vehicle system , 2016 .

[26]  Alain Charcosset,et al.  Genome-Wide Analysis of Yield in Europe: Allelic Effects Vary with Drought and Heat Scenarios1[OPEN] , 2016, Plant Physiology.

[27]  T. Kazic,et al.  An opinion on imaging challenges in phenotyping field crops , 2016, Machine Vision and Applications.

[28]  YangQuan Chen,et al.  An analysis of the effect of the bidirectional reflectance distribution function on remote sensing imagery accuracy from Small Unmanned Aircraft Systems , 2016, 2016 International Conference on Unmanned Aircraft Systems (ICUAS).

[29]  Tony P. Pridmore,et al.  Deep machine learning provides state-of-the-art performance in image-based plant phenotyping , 2016, bioRxiv.

[30]  Naiqian Zhang,et al.  Development and Deployment of a Portable Field Phenotyping Platform , 2016 .

[31]  Yunbi Xu,et al.  Envirotyping for deciphering environmental impacts on crop plants , 2016, Theoretical and Applied Genetics.

[32]  Xu Wang,et al.  Development of a field-based high-throughput mobile phenotyping platform , 2016, Comput. Electron. Agric..

[33]  Ashutosh Kumar Singh,et al.  Machine Learning for High-Throughput Stress Phenotyping in Plants. , 2016, Trends in plant science.

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

[35]  Malcolm J. Hawkesford,et al.  Plant phenotyping: increasing throughput and precision at multiple scales. , 2016, Functional plant biology : FPB.

[36]  Seishi Ninomiya,et al.  Comparison of ground cover estimates from experiment plots in cotton, sorghum and sugarcane based on images and ortho-mosaics captured by UAV. , 2016, Functional plant biology : FPB.

[37]  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.

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

[39]  Yoshio Inoue,et al.  The Impact of Sunlight Conditions on the Consistency of Vegetation Indices in Croplands - Effective Usage of Vegetation Indices from Continuous Ground-Based Spectral Measurements , 2015, Remote. Sens..

[40]  S. Sankaran,et al.  Low-altitude, high-resolution aerial imaging systems for row and field crop phenotyping: A review , 2015 .

[41]  Sarun Sumriddetchkajorn,et al.  Android-based rice leaf color analyzer for estimating the needed amount of nitrogen fertilizer , 2015, Comput. Electron. Agric..

[42]  Grégoire M. Hummel,et al.  LeasyScan: a novel concept combining 3D imaging and lysimetry for high-throughput phenotyping of traits controlling plant water budget , 2015, Journal of experimental botany.

[43]  Heiner Kuhlmann,et al.  Accuracy Analysis of a Multi-View Stereo Approach for Phenotyping of Tomato Plants at the Organ Level , 2015, Sensors.

[44]  H. Scharr,et al.  The leaf angle distribution of natural plant populations: assessing the canopy with a novel software tool , 2015, Plant Methods.

[45]  S. Ninomiya,et al.  Automated characterization of flowering dynamics in rice using field-acquired time-series RGB images , 2015, Plant Methods.

[46]  C. Messina,et al.  Breeding drought-tolerant maize hybrids for the US corn-belt: discovery to product. , 2014, Journal of experimental botany.

[47]  Giovanni Bitella,et al.  A Novel Low-Cost Open-Hardware Platform for Monitoring Soil Water Content and Multiple Soil-Air-Vegetation Parameters , 2014, Sensors.

[48]  F. Baret,et al.  Green area index from an unmanned aerial system over wheat and rapeseed crops , 2014 .

[49]  Jose A. Jiménez-Berni,et al.  Proximal Remote Sensing Buggies and Potential Applications for Field-Based Phenotyping , 2014 .

[50]  Svend Christensen,et al.  Development of a Mobile Multispectral Imaging Platform for Precise Field Phenotyping , 2014 .

[51]  Arnold J. Bloom,et al.  Easy Leaf Area: Automated digital image analysis for rapid and accurate measurement of leaf area1 , 2014, Applications in plant sciences.

[52]  Emmanuelle Gouillart,et al.  scikit-image: image processing in Python , 2014, PeerJ.

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

[54]  R. Richards,et al.  Improvement of crop yield in dry environments: benchmarks, levels of organisation and the role of nitrogen. , 2014, Journal of experimental botany.

[55]  Christian Klukas,et al.  Integrated Analysis Platform: An Open-Source Information System for High-Throughput Plant Phenotyping1[C][W][OPEN] , 2014, Plant Physiology.

[56]  G. Hammer,et al.  Characterizing drought stress and trait influence on maize yield under current and future conditions , 2014, Global change biology.

[57]  Raffaele Casa,et al.  Development of an app for estimating leaf area index using a smartphone. Trueness and precision determination and comparison with other indirect methods , 2013 .

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

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

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

[61]  Jane Hunter,et al.  An ontology-centric architecture for extensible scientific data management systems , 2013, Future Gener. Comput. Syst..

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

[63]  Ky L. Mathews,et al.  Environment characterization as an aid to wheat improvement: interpreting genotype-environment interactions by modelling water-deficit patterns in North-Eastern Australia. , 2011, Journal of experimental botany.

[64]  David Gouache,et al.  Why are wheat yields stagnating in Europe? A comprehensive data analysis for France , 2010 .

[65]  G. Hammer,et al.  Modeling QTL for complex traits: detection and context for plant breeding. , 2009, Current opinion in plant biology.

[66]  Pablo J. Zarco-Tejada,et al.  Thermal and Narrowband Multispectral Remote Sensing for Vegetation Monitoring From an Unmanned Aerial Vehicle , 2009, IEEE Transactions on Geoscience and Remote Sensing.

[67]  L. Maurer,et al.  Be flexible , 2008, IEEE Microwave Magazine.

[68]  Frédéric Baret,et al.  Review of methods for in situ leaf area index determination Part I. Theories, sensors and hemispherical photography , 2004 .

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

[70]  Peter Langridge,et al.  Physiological breeding. , 2016, Current opinion in plant biology.

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

[72]  Tony P. Pridmore,et al.  Imaging Methods for Phenotyping of Plant Traits , 2015 .

[73]  R. Siegwart,et al.  Studying Phenotypic Variability in Crops using a Hand-held Sensor Platform , 2015 .

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