Phenotyping Fusarium head blight through seed morphology characteristics using RGB imaging

Fusarium head blight (FHB) is an economically important disease affecting wheat and thus poses a major threat to wheat production. Several studies have evaluated the effectiveness of image analysis methods to predict FHB using disease-infected grains; however, few have looked at the final applicatio...

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Autores: Leiva, F., Zakieh, M., Alamrani, M., Dhakal, R., Henriksson, T., Singh, P.K., Chawade, A.
Formato: artículo
Estado:Versión publicada
Fecha de publicación:2022
País:México
Recursos:Centro Internacional de Mejoramiento de Maíz y Trigo
Repositorio:Repositorio Institucional de Publicaciones Multimedia del CIMMYT
OAI Identifier:oai:repository.cimmyt.org:10883/22302
Acesso em linha:https://hdl.handle.net/10883/22302
Access Level:acceso abierto
Palavra-chave:AGRICULTURAL SCIENCES AND BIOTECHNOLOGY
Fusarium Head Blight
Seed Phenotyping
Seed Morphological Characters
Visual Scores
Smart Grain
Cgrain Value
FUSARIUM
SEED
WHEAT
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oai_identifier_str oai:repository.cimmyt.org:10883/22302
network_acronym_str MX
network_name_str México
repository_id_str
spelling Phenotyping Fusarium head blight through seed morphology characteristics using RGB imagingLeiva, F.Zakieh, M.Alamrani, M.Dhakal, R.Henriksson, T.Singh, P.K.Chawade, A.AGRICULTURAL SCIENCES AND BIOTECHNOLOGYFusarium Head BlightSeed PhenotypingSeed Morphological CharactersVisual ScoresSmart GrainCgrain ValueFUSARIUMSEEDWHEATFusarium head blight (FHB) is an economically important disease affecting wheat and thus poses a major threat to wheat production. Several studies have evaluated the effectiveness of image analysis methods to predict FHB using disease-infected grains; however, few have looked at the final application, considering the relationship between cost and benefit, resolution, and accuracy. The conventional screening of FHB resistance of large-scale samples is still dependent on low-throughput visual inspections. This study aims to compare the performance of two cost–benefit seed image analysis methods, the free software “SmartGrain” and the fully automated commercially available instrument “Cgrain Value™” by assessing 16 seed morphological traits of winter wheat to predict FHB. The analysis was carried out on a seed set of FHB which was visually assessed as to the severity. The dataset is composed of 432 winter wheat genotypes that were greenhouse-inoculated. The predictions from each method, in addition to the predictions combined from the results of both methods, were compared with the disease visual scores. The results showed that Cgrain Value™ had a higher prediction accuracy of R2 = 0.52 compared with SmartGrain for which R2 = 0.30 for all morphological traits. However, the results combined from both methods showed the greatest prediction performance of R2 = 0.58. Additionally, a subpart of the morphological traits, namely, width, length, thickness, and color features, showed a higher correlation with the visual scores compared with the other traits. Overall, both methods were related to the visual scores. This study shows that these affordable imaging methods could be effective to predict FHB in seeds and enable us to distinguish minor differences in seed morphology, which could lead to a precise performance selection of disease-free seeds/grains.Frontiers2022-12-07T20:30:17Z2022-12-07T20:30:17Z2022Published Versioninfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/10883/2230210.3389/fpls.2022.1010249131664-462XFrontiers in Plant Science1010249reponame:Repositorio Institucional de Publicaciones Multimedia del CIMMYTinstname:Centro Internacional de Mejoramiento de Maíz y Trigoinstacron:CIMMYTEnglishNutrition, health & food securityAccelerated BreedingGenetic InnovationSLU GrogrundNordic Council of MinistersNordForskhttps://hdl.handle.net/10568/126508SwitzerlandCIMMYT manages Intellectual Assets as International Public Goods. The user is free to download, print, store and share this work. In case you want to translate or create any other derivative work and share or distribute such translation/derivative work, please contact CIMMYT-Knowledge-Center@cgiar.org indicating the work you want to use and the kind of use you intend; CIMMYT will contact you with the suitable license for that purposeOpen Accessinfo:eu-repo/semantics/openAccessoai:repository.cimmyt.org:10883/223022024-10-11T19:57:53Z
dc.title.none.fl_str_mv Phenotyping Fusarium head blight through seed morphology characteristics using RGB imaging
title Phenotyping Fusarium head blight through seed morphology characteristics using RGB imaging
spellingShingle Phenotyping Fusarium head blight through seed morphology characteristics using RGB imaging
Leiva, F.
AGRICULTURAL SCIENCES AND BIOTECHNOLOGY
Fusarium Head Blight
Seed Phenotyping
Seed Morphological Characters
Visual Scores
Smart Grain
Cgrain Value
FUSARIUM
SEED
WHEAT
title_short Phenotyping Fusarium head blight through seed morphology characteristics using RGB imaging
title_full Phenotyping Fusarium head blight through seed morphology characteristics using RGB imaging
title_fullStr Phenotyping Fusarium head blight through seed morphology characteristics using RGB imaging
title_full_unstemmed Phenotyping Fusarium head blight through seed morphology characteristics using RGB imaging
title_sort Phenotyping Fusarium head blight through seed morphology characteristics using RGB imaging
dc.creator.none.fl_str_mv Leiva, F.
Zakieh, M.
Alamrani, M.
Dhakal, R.
Henriksson, T.
Singh, P.K.
Chawade, A.
author Leiva, F.
author_facet Leiva, F.
Zakieh, M.
Alamrani, M.
Dhakal, R.
Henriksson, T.
Singh, P.K.
Chawade, A.
author_role author
author2 Zakieh, M.
Alamrani, M.
Dhakal, R.
Henriksson, T.
Singh, P.K.
Chawade, A.
author2_role author
author
author
author
author
author
dc.subject.none.fl_str_mv AGRICULTURAL SCIENCES AND BIOTECHNOLOGY
Fusarium Head Blight
Seed Phenotyping
Seed Morphological Characters
Visual Scores
Smart Grain
Cgrain Value
FUSARIUM
SEED
WHEAT
topic AGRICULTURAL SCIENCES AND BIOTECHNOLOGY
Fusarium Head Blight
Seed Phenotyping
Seed Morphological Characters
Visual Scores
Smart Grain
Cgrain Value
FUSARIUM
SEED
WHEAT
description Fusarium head blight (FHB) is an economically important disease affecting wheat and thus poses a major threat to wheat production. Several studies have evaluated the effectiveness of image analysis methods to predict FHB using disease-infected grains; however, few have looked at the final application, considering the relationship between cost and benefit, resolution, and accuracy. The conventional screening of FHB resistance of large-scale samples is still dependent on low-throughput visual inspections. This study aims to compare the performance of two cost–benefit seed image analysis methods, the free software “SmartGrain” and the fully automated commercially available instrument “Cgrain Value™” by assessing 16 seed morphological traits of winter wheat to predict FHB. The analysis was carried out on a seed set of FHB which was visually assessed as to the severity. The dataset is composed of 432 winter wheat genotypes that were greenhouse-inoculated. The predictions from each method, in addition to the predictions combined from the results of both methods, were compared with the disease visual scores. The results showed that Cgrain Value™ had a higher prediction accuracy of R2 = 0.52 compared with SmartGrain for which R2 = 0.30 for all morphological traits. However, the results combined from both methods showed the greatest prediction performance of R2 = 0.58. Additionally, a subpart of the morphological traits, namely, width, length, thickness, and color features, showed a higher correlation with the visual scores compared with the other traits. Overall, both methods were related to the visual scores. This study shows that these affordable imaging methods could be effective to predict FHB in seeds and enable us to distinguish minor differences in seed morphology, which could lead to a precise performance selection of disease-free seeds/grains.
publishDate 2022
dc.date.none.fl_str_mv 2022-12-07T20:30:17Z
2022-12-07T20:30:17Z
2022
dc.type.none.fl_str_mv Published Version
info:eu-repo/semantics/publishedVersion
info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://hdl.handle.net/10883/22302
10.3389/fpls.2022.1010249
url https://hdl.handle.net/10883/22302
identifier_str_mv 10.3389/fpls.2022.1010249
dc.language.none.fl_str_mv English
language_invalid_str_mv English
dc.relation.none.fl_str_mv Nutrition, health & food security
Accelerated Breeding
Genetic Innovation
SLU Grogrund
Nordic Council of Ministers
NordForsk
https://hdl.handle.net/10568/126508
dc.rights.none.fl_str_mv Open Access
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Open Access
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.coverage.none.fl_str_mv Switzerland
dc.publisher.none.fl_str_mv Frontiers
publisher.none.fl_str_mv Frontiers
dc.source.none.fl_str_mv 13
1664-462X
Frontiers in Plant Science
1010249
reponame:Repositorio Institucional de Publicaciones Multimedia del CIMMYT
instname:Centro Internacional de Mejoramiento de Maíz y Trigo
instacron:CIMMYT
instname_str Centro Internacional de Mejoramiento de Maíz y Trigo
instacron_str CIMMYT
institution CIMMYT
reponame_str Repositorio Institucional de Publicaciones Multimedia del CIMMYT
collection Repositorio Institucional de Publicaciones Multimedia del CIMMYT
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15.812429

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