Anyela V Camargo Rodriguez

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John H DoonanProfessor of Genetics, National Plant Phenomics Centre, Aberystwyth UniversityVerified email at aber.ac.uk
Francisco AzuajeGenomics EnglandVerified email at genomicsengland.co.uk
Gustavo A. LobosAssociate Professor, Universidad de Talca, ChileVerified email at Utalca.cl
Fiona CorkeAberystwyth UniversityVerified email at aber.ac.uk
Alan GayInstitute of Biological Environmental and Rural Sciences, Aberystwyth UniversityVerified email at aber.ac.uk
Araus, José LuisUniversity of BarcelonaVerified email at ub.edu
Alison R BentleyCIMMYTVerified email at cgiar.org
Richard MottWeldon Professor of Computational Genetics, University College London, UKVerified email at ucl.ac.uk
Pachepsky YUSDA-ARSVerified email at ars.usda.gov
Rodomiro OrtizProfessor, Genetics & Plant Breeding, Swedish University of Agricultural SciencesVerified email at slu.se
Konstantinos (Kostas) VlachonasiosProfessor, School of Biology, Department of Botany, Aristotle University of ThessalonikiVerified email at bio.auth.gr
Eric OberNational Institute of Agricultural Botany (NIAB)Verified email at niab.com
John Clifton-BrownJustus Liebig University GießenVerified email at agrar.uni-giessen.de
Niall McNamaraGroup Leader, Plant Soil Interactions, Centre for Ecology & HydrologyVerified email at ceh.ac.uk
Jennifer DungaitUniversity of ExeterVerified email at exeter.ac.uk
Professor Rattan S YadavProfessor of Plant Genetics, Aberystwyth UniversityVerified email at aber.ac.uk
Christina L MarleyReader of Agricultural ScienceVerified email at aber.ac.uk
Mike HumphreysGroup Leader, Aberystwyth UniversityVerified email at aber.ac.uk
John M. HancockIndependent/University of Ljubljana (Visiting Professor)Verified email at elixir-europe.org
Jorge Cadena TorresAgrosavia, Investigador PhD, Centro de Investigaciones TuripanaVerified email at agrosavia.co

Anyela V Camargo Rodriguez

Unknown affiliation

Verified email at fiscrop.com

Predictive modelling Statistical Genetics Machine Learning


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
An image-processing based algorithm to automatically identify plant disease visual symptoms A Camargo, JS Smith Biosystems engineering 102 (1), 9-21, 2009	591	2009
Image pattern classification for the identification of disease causing agents in plants A Camargo, JS Smith Computers and electronics in agriculture 66 (2), 121-125, 2009	414	2009
Encyclopedia of agrophysics WEH Blum, J de Baerdemaeker, CW Finkl, R Horn, Y Pachepsky, ... Springer Science & Business Media, 2011	126	2011
Permutation–based statistical tests for multiple hypotheses A Camargo, F Azuaje, H Wang, H Zheng Source code for biology and medicine 3, 1-8, 2008	123	2008
Plant phenotyping and phenomics for plant breeding GA Lobos, AV Camargo, A Del Pozo, JL Araus, R Ortiz, JH Doonan Frontiers in plant science 8, 313980, 2017	84	2017
Linking gene expression and functional network data in human heart failure A Camargo, F Azuaje PLoS One 2 (12), e1347, 2007	55	2007
Identification of dilated cardiomyopathy signature genes through gene expression and network data integration A Camargo, F Azuaje Genomics 92 (6), 404-413, 2008	53	2008
Functional mapping of quantitative trait loci (QTLs) associated with plant performance in a wheat MAGIC mapping population AV Camargo, I Mackay, R Mott, J Han, JH Doonan, K Askew, F Corke, ... Frontiers in plant science 9, 360778, 2018	40	2018
Determining phenological patterns associated with the onset of senescence in a wheat MAGIC mapping population AV Camargo, R Mott, KA Gardner, IJ Mackay, F Corke, JH Doonan, ... Frontiers in Plant Science 7, 1540, 2016	39	2016
Objective definition of rosette shape variation using a combined computer vision and data mining approach A Camargo, D Papadopoulou, Z Spyropoulou, K Vlachonasios, ... PloS one 9 (5), e96889, 2014	39	2014
Genotypic variations in leaf and whole-plant water use efficiencies are closely related in bread wheat genotypes under well-watered and water-limited conditions during grain … A Del Pozo, AM Méndez-Espinoza, S Romero-Bravo, M Garriga, ... Scientific Reports 10 (1), 460, 2020	33	2020
Latin America: a development pole for phenomics AV Camargo, GA Lobos Frontiers in Plant Science 7, 209315, 2016	30	2016
AquaCropR: Crop Growth Model for R AVC Rodriguez, ES Ober Agronomy 9 (378), 2019	24*	2019
Predicting future biomass yield in Miscanthus using the carbohydrate metabolic profile as a biomarker AL Maddison, A Camargo‐Rodriguez, IM Scott, CM Jones, DMO Elias, ... Gcb Bioenergy 9 (7), 1264-1278, 2017	18	2017
Root imaging showing comparisons in root distribution and ontogeny in novel Festulolium populations and closely related perennial ryegrass varieties MW Humphreys, JH Doonan, R Boyle, AC Rodriguez, CL Marley, ... Food and Energy Security, e00145, 2018	17	2018
Bio-based value chains potential in the management of cacao pod waste in Colombia, a case study DC Meza-Sepúlveda, AM Castro, A Zamora, JW Arboleda, AM Gallego, ... Agronomy 11 (4), 693, 2021	14	2021
Emerging Advanced Technologies to Mitigate the Impact of Climate Change in Africa P Francisco Ribeiro, AV Camargo Rodriguez Plants 9 (3), 381, 2020	13	2020
Phenomics JM Hancock CRC Press, 2014	13	2014
Challenges of crop phenomics in the post-genomic era V Gegas, A Gay, A Camargo, J Doonan Phenomics 142, 171, 2014	13	2014
Intelligent systems for the assessment of crop disorders A Camargo, JP Molina, J Cadena-Torres, N Jimenez, JT Kim Computers and electronics in agriculture 85, 1-7, 2012	12	2012

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