Wheat ear counting in-field conditions: high throughput and low-cost approach using RGB images
Background The number of ears per unit ground area (ear density) is one of the main agronomic yield components in determining grain yield in wheat. A fast evaluation of this attribute may contribute to monitoring the efficiency of crop management practices, to an early prediction of grain yield or a...
| Autores: | , , , , |
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| Formato: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2018 |
| País: | España |
| Recursos: | Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya) |
| Repositorio: | Recercat. Dipósit de la Recerca de Catalunya |
| OAI Identifier: | oai:recercat.cat:2445/134864 |
| Acesso em linha: | https://hdl.handle.net/2445/134864 |
| Access Level: | acceso abierto |
| Palavra-chave: | Blat Conreus Crops Wheat |
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Wheat ear counting in-field conditions: high throughput and low-cost approach using RGB imagesFernández Gallego, José A.Kefauver, Shawn CarlisleAparicio Gutiérrez, NievesNieto Taladriz, María TeresaAraus Ortega, José LuisBlatConreusCropsWheatBackground The number of ears per unit ground area (ear density) is one of the main agronomic yield components in determining grain yield in wheat. A fast evaluation of this attribute may contribute to monitoring the efficiency of crop management practices, to an early prediction of grain yield or as a phenotyping trait in breeding programs. Currently the number of ears is counted manually, which is time consuming. Moreover, there is no single standardized protocol for counting the ears. An automatic ear-counting algorithm is proposed to estimate ear density under field conditions based on zenithal color digital images taken from above the crop in natural light conditions. Field trials were carried out at two sites in Spain during the 2014/2015 crop season on a set of 24 varieties of durum wheat with two growing conditions per site. The algorithm for counting uses three steps: (1) a Laplacian frequency filter chosen to remove low and high frequency elements appearing in an image, (2) a Median filter to reduce high noise still present around the ears and (3) segmentation using Find Maxima to segment local peaks and determine the ear count within the image. Results The results demonstrate high success rate (higher than 90%) between the algorithm counts and the manual (image-based) ear counts, and precision, with a low standard deviation (around 5%). The relationships between algorithm ear counts and grain yield was also significant and greater than the correlation with manual (field-based) ear counts. In this approach, results demonstrate that automatic ear counting performed on data captured around anthesis correlated better with grain yield than with images captured at later stages when the low performance of ear counting at late grain filling stages was associated with the loss of contrast between canopy and ears. Conclusions Developing robust, low-cost and efficient field methods to assess wheat ear density, as a major agronomic component of yield, is highly relevant for phenotyping efforts towards increases in grain yield. Although the phenological stage of measurements is important, the robust image analysis algorithm presented here appears to be amenable from aerial or other automated platforms.BioMed Central2019201920182019info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersion12 p.application/pdfhttps://hdl.handle.net/2445/134864Articles publicats en revistes (Biologia Evolutiva, Ecologia i Ciències Ambientals)reponame:Recercat. Dipósit de la Recerca de Catalunyainstname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)InglésReproducció del document publicat a: https://doi.org/10.1186/s13007-018-0289-4Plant Methods, 2018, vol. 14, num. 22https://doi.org/10.1186/s13007-018-0289-4cc-by (c) Fernández Gallego, Jose A. et al., 2018http://creativecommons.org/licenses/by/3.0/esinfo:eu-repo/semantics/openAccessoai:recercat.cat:2445/1348642026-05-29T05:05:01Z |
| dc.title.none.fl_str_mv |
Wheat ear counting in-field conditions: high throughput and low-cost approach using RGB images |
| title |
Wheat ear counting in-field conditions: high throughput and low-cost approach using RGB images |
| spellingShingle |
Wheat ear counting in-field conditions: high throughput and low-cost approach using RGB images Fernández Gallego, José A. Blat Conreus Crops Wheat |
| title_short |
Wheat ear counting in-field conditions: high throughput and low-cost approach using RGB images |
| title_full |
Wheat ear counting in-field conditions: high throughput and low-cost approach using RGB images |
| title_fullStr |
Wheat ear counting in-field conditions: high throughput and low-cost approach using RGB images |
| title_full_unstemmed |
Wheat ear counting in-field conditions: high throughput and low-cost approach using RGB images |
| title_sort |
Wheat ear counting in-field conditions: high throughput and low-cost approach using RGB images |
| dc.creator.none.fl_str_mv |
Fernández Gallego, José A. Kefauver, Shawn Carlisle Aparicio Gutiérrez, Nieves Nieto Taladriz, María Teresa Araus Ortega, José Luis |
| author |
Fernández Gallego, José A. |
| author_facet |
Fernández Gallego, José A. Kefauver, Shawn Carlisle Aparicio Gutiérrez, Nieves Nieto Taladriz, María Teresa Araus Ortega, José Luis |
| author_role |
author |
| author2 |
Kefauver, Shawn Carlisle Aparicio Gutiérrez, Nieves Nieto Taladriz, María Teresa Araus Ortega, José Luis |
| author2_role |
author author author author |
| dc.subject.none.fl_str_mv |
Blat Conreus Crops Wheat |
| topic |
Blat Conreus Crops Wheat |
| description |
Background The number of ears per unit ground area (ear density) is one of the main agronomic yield components in determining grain yield in wheat. A fast evaluation of this attribute may contribute to monitoring the efficiency of crop management practices, to an early prediction of grain yield or as a phenotyping trait in breeding programs. Currently the number of ears is counted manually, which is time consuming. Moreover, there is no single standardized protocol for counting the ears. An automatic ear-counting algorithm is proposed to estimate ear density under field conditions based on zenithal color digital images taken from above the crop in natural light conditions. Field trials were carried out at two sites in Spain during the 2014/2015 crop season on a set of 24 varieties of durum wheat with two growing conditions per site. The algorithm for counting uses three steps: (1) a Laplacian frequency filter chosen to remove low and high frequency elements appearing in an image, (2) a Median filter to reduce high noise still present around the ears and (3) segmentation using Find Maxima to segment local peaks and determine the ear count within the image. Results The results demonstrate high success rate (higher than 90%) between the algorithm counts and the manual (image-based) ear counts, and precision, with a low standard deviation (around 5%). The relationships between algorithm ear counts and grain yield was also significant and greater than the correlation with manual (field-based) ear counts. In this approach, results demonstrate that automatic ear counting performed on data captured around anthesis correlated better with grain yield than with images captured at later stages when the low performance of ear counting at late grain filling stages was associated with the loss of contrast between canopy and ears. Conclusions Developing robust, low-cost and efficient field methods to assess wheat ear density, as a major agronomic component of yield, is highly relevant for phenotyping efforts towards increases in grain yield. Although the phenological stage of measurements is important, the robust image analysis algorithm presented here appears to be amenable from aerial or other automated platforms. |
| publishDate |
2018 |
| dc.date.none.fl_str_mv |
2018 2019 2019 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/2445/134864 |
| url |
https://hdl.handle.net/2445/134864 |
| 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/10.1186/s13007-018-0289-4 Plant Methods, 2018, vol. 14, num. 22 https://doi.org/10.1186/s13007-018-0289-4 |
| dc.rights.none.fl_str_mv |
cc-by (c) Fernández Gallego, Jose A. et al., 2018 http://creativecommons.org/licenses/by/3.0/es info:eu-repo/semantics/openAccess |
| rights_invalid_str_mv |
cc-by (c) Fernández Gallego, Jose A. et al., 2018 http://creativecommons.org/licenses/by/3.0/es |
| eu_rights_str_mv |
openAccess |
| dc.format.none.fl_str_mv |
12 p. application/pdf |
| dc.publisher.none.fl_str_mv |
BioMed Central |
| publisher.none.fl_str_mv |
BioMed Central |
| dc.source.none.fl_str_mv |
Articles publicats en revistes (Biologia Evolutiva, Ecologia i Ciències Ambientals) reponame:Recercat. Dipósit de la Recerca de Catalunya instname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya) |
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Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya) |
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Recercat. Dipósit de la Recerca de Catalunya |
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Recercat. Dipósit de la Recerca de Catalunya |
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