Cereal crop ear counting in field conditions using zenithal RGB images
Ear density, or the number of ears per square meter (ears/m2), is a central focus in many cereal crop breeding programs, such as wheat andbarley, representing an important agronomic yield component for estimating grain yield. Therefore, a quick, efficient, and standardized techniquefor assessing ear...
| Autores: | , , , , , , , , , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2019 |
| País: | España |
| Institución: | Universidad de Barcelona |
| Repositorio: | Dipòsit Digital de la UB |
| OAI Identifier: | oai:diposit.ub.edu:2445/152612 |
| Acceso en línea: | https://hdl.handle.net/2445/152612 |
| Access Level: | acceso abierto |
| Palabra clave: | Cereals Agricultura Fenotip Imatges Crops Conreus Agriculture Phenotype Pictures |
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Cereal crop ear counting in field conditions using zenithal RGB imagesFernández Gallego, José A.Buchaillot, María LuisaGracia-Romero, AdrianVatter, ThomasVergara Díaz, OmarAparicio Gutiérrez, NievesNieto Taladriz, María TeresaKerfal, SamirSerret Molins, M. DolorsAraus Ortega, José LuisKefauver, Shawn CarlisleCerealsAgriculturaFenotipImatgesCropsConreusCerealsAgriculturePhenotypePicturesEar density, or the number of ears per square meter (ears/m2), is a central focus in many cereal crop breeding programs, such as wheat andbarley, representing an important agronomic yield component for estimating grain yield. Therefore, a quick, efficient, and standardized techniquefor assessing ear density would aid in improving agricultural management, providing improvements in preharvest yield predictions, or could evenbe used as a tool for crop breeding when it has been defined as a trait of importance. Not only are the current techniques for manual ear densityassessments laborious and time-consuming, but they are also without any official standardized protocol, whether by linear meter, area quadrant,or an extrapolation based on plant ear density and plant counts postharvest. An automatic ear counting algorithm is presented in detail forestimating ear density with only sunlight illumination in field conditions based on zenithal (nadir) natural color (red, green, and blue [RGB]) digitalimages, allowing for high-throughput standardized measurements. Different field trials of durum wheat and barley distributed geographicallyacross Spain during the 2014/2015 and 2015/2016 crop seasons in irrigated and rainfed trials were used to provide representative results. Thethree-phase protocol includes crop growth stage and field condition planning, image capture guidelines, and a computer algorithm of three steps:(i) a Laplacian frequency filter to remove low- and high-frequency artifacts, (ii) a median filter to reduce high noise, and (iii) segmentation andcounting using local maxima peaks for the final count. Minor adjustments to the algorithm code must be made corresponding to the cameraresolution, focal length, and distance between the camera and the crop canopy. The results demonstrate a high success rate (higher than 90%)and R2 values (of 0.62-0.75) between the algorithm counts and the manual image-based ear counts for both durum wheat and barley.JoVE2019info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://hdl.handle.net/2445/152612Articles publicats en revistes (Biologia Evolutiva, Ecologia i Ciències Ambientals)reponame:Dipòsit Digital de la UBinstname:Universidad de BarcelonaInglésReproducció del document publicat a: https://doi.org/10.3791/58695JoVE. Journal of Visualized Experiments, 2019, vol. 144 , p. e58695https://doi.org/10.3791/58695(c) JoVE, 2019info:eu-repo/semantics/openAccessoai:diposit.ub.edu:2445/1526122026-05-27T06:46:51Z |
| dc.title.none.fl_str_mv |
Cereal crop ear counting in field conditions using zenithal RGB images |
| title |
Cereal crop ear counting in field conditions using zenithal RGB images |
| spellingShingle |
Cereal crop ear counting in field conditions using zenithal RGB images Fernández Gallego, José A. Cereals Agricultura Fenotip Imatges Crops Conreus Cereals Agriculture Phenotype Pictures |
| title_short |
Cereal crop ear counting in field conditions using zenithal RGB images |
| title_full |
Cereal crop ear counting in field conditions using zenithal RGB images |
| title_fullStr |
Cereal crop ear counting in field conditions using zenithal RGB images |
| title_full_unstemmed |
Cereal crop ear counting in field conditions using zenithal RGB images |
| title_sort |
Cereal crop ear counting in field conditions using zenithal RGB images |
| dc.creator.none.fl_str_mv |
Fernández Gallego, José A. Buchaillot, María Luisa Gracia-Romero, Adrian Vatter, Thomas Vergara Díaz, Omar Aparicio Gutiérrez, Nieves Nieto Taladriz, María Teresa Kerfal, Samir Serret Molins, M. Dolors Araus Ortega, José Luis Kefauver, Shawn Carlisle |
| author |
Fernández Gallego, José A. |
| author_facet |
Fernández Gallego, José A. Buchaillot, María Luisa Gracia-Romero, Adrian Vatter, Thomas Vergara Díaz, Omar Aparicio Gutiérrez, Nieves Nieto Taladriz, María Teresa Kerfal, Samir Serret Molins, M. Dolors Araus Ortega, José Luis Kefauver, Shawn Carlisle |
| author_role |
author |
| author2 |
Buchaillot, María Luisa Gracia-Romero, Adrian Vatter, Thomas Vergara Díaz, Omar Aparicio Gutiérrez, Nieves Nieto Taladriz, María Teresa Kerfal, Samir Serret Molins, M. Dolors Araus Ortega, José Luis Kefauver, Shawn Carlisle |
| author2_role |
author author author author author author author author author author |
| dc.subject.none.fl_str_mv |
Cereals Agricultura Fenotip Imatges Crops Conreus Cereals Agriculture Phenotype Pictures |
| topic |
Cereals Agricultura Fenotip Imatges Crops Conreus Cereals Agriculture Phenotype Pictures |
| description |
Ear density, or the number of ears per square meter (ears/m2), is a central focus in many cereal crop breeding programs, such as wheat andbarley, representing an important agronomic yield component for estimating grain yield. Therefore, a quick, efficient, and standardized techniquefor assessing ear density would aid in improving agricultural management, providing improvements in preharvest yield predictions, or could evenbe used as a tool for crop breeding when it has been defined as a trait of importance. Not only are the current techniques for manual ear densityassessments laborious and time-consuming, but they are also without any official standardized protocol, whether by linear meter, area quadrant,or an extrapolation based on plant ear density and plant counts postharvest. An automatic ear counting algorithm is presented in detail forestimating ear density with only sunlight illumination in field conditions based on zenithal (nadir) natural color (red, green, and blue [RGB]) digitalimages, allowing for high-throughput standardized measurements. Different field trials of durum wheat and barley distributed geographicallyacross Spain during the 2014/2015 and 2015/2016 crop seasons in irrigated and rainfed trials were used to provide representative results. Thethree-phase protocol includes crop growth stage and field condition planning, image capture guidelines, and a computer algorithm of three steps:(i) a Laplacian frequency filter to remove low- and high-frequency artifacts, (ii) a median filter to reduce high noise, and (iii) segmentation andcounting using local maxima peaks for the final count. Minor adjustments to the algorithm code must be made corresponding to the cameraresolution, focal length, and distance between the camera and the crop canopy. The results demonstrate a high success rate (higher than 90%)and R2 values (of 0.62-0.75) between the algorithm counts and the manual image-based ear counts for both durum wheat and barley. |
| publishDate |
2019 |
| dc.date.none.fl_str_mv |
2019 |
| dc.type.none.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion |
| format |
article |
| status_str |
publishedVersion |
| dc.identifier.none.fl_str_mv |
https://hdl.handle.net/2445/152612 |
| url |
https://hdl.handle.net/2445/152612 |
| dc.language.none.fl_str_mv |
Inglés |
| language_invalid_str_mv |
Inglés |
| dc.relation.none.fl_str_mv |
Reproducció del document publicat a: https://doi.org/10.3791/58695 JoVE. Journal of Visualized Experiments, 2019, vol. 144 , p. e58695 https://doi.org/10.3791/58695 |
| dc.rights.none.fl_str_mv |
(c) JoVE, 2019 info:eu-repo/semantics/openAccess |
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(c) JoVE, 2019 |
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openAccess |
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application/pdf |
| dc.publisher.none.fl_str_mv |
JoVE |
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JoVE |
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Articles publicats en revistes (Biologia Evolutiva, Ecologia i Ciències Ambientals) reponame:Dipòsit Digital de la UB instname:Universidad de Barcelona |
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Universidad de Barcelona |
| reponame_str |
Dipòsit Digital de la UB |
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Dipòsit Digital de la UB |
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