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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Gespeichert in:

Autor*in:	Fernanda Leiva [verfasserIn] Mustafa Zakieh [verfasserIn] Marwan Alamrani [verfasserIn] Rishap Dhakal [verfasserIn] Tina Henriksson [verfasserIn] Pawan Kumar Singh [verfasserIn] Aakash Chawade [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	Fusarium head blight seed phenotyping seed morphological characters wheat visual scores SmartGrain

Übergeordnetes Werk:	In: Frontiers in Plant Science - Frontiers Media S.A., 2011, 13(2022)
Übergeordnetes Werk:	volume:13 ; year:2022

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3389/fpls.2022.1010249

Katalog-ID:	DOAJ086959565

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520			\|a 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.
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spelling	10.3389/fpls.2022.1010249 doi (DE-627)DOAJ086959565 (DE-599)DOAJ692c4eed62da40ce9d248770859da942 DE-627 ger DE-627 rakwb eng SB1-1110 Fernanda Leiva verfasserin aut Phenotyping Fusarium head blight through seed morphology characteristics using RGB imaging 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. Fusarium head blight seed phenotyping seed morphological characters wheat visual scores SmartGrain Plant culture Mustafa Zakieh verfasserin aut Marwan Alamrani verfasserin aut Rishap Dhakal verfasserin aut Tina Henriksson verfasserin aut Pawan Kumar Singh verfasserin aut Aakash Chawade verfasserin aut In Frontiers in Plant Science Frontiers Media S.A., 2011 13(2022) (DE-627)662359240 (DE-600)2613694-6 1664462X nnns volume:13 year:2022 https://doi.org/10.3389/fpls.2022.1010249 kostenfrei https://doaj.org/article/692c4eed62da40ce9d248770859da942 kostenfrei https://www.frontiersin.org/articles/10.3389/fpls.2022.1010249/full kostenfrei https://doaj.org/toc/1664-462X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 13 2022
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allfieldsSound	10.3389/fpls.2022.1010249 doi (DE-627)DOAJ086959565 (DE-599)DOAJ692c4eed62da40ce9d248770859da942 DE-627 ger DE-627 rakwb eng SB1-1110 Fernanda Leiva verfasserin aut Phenotyping Fusarium head blight through seed morphology characteristics using RGB imaging 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. Fusarium head blight seed phenotyping seed morphological characters wheat visual scores SmartGrain Plant culture Mustafa Zakieh verfasserin aut Marwan Alamrani verfasserin aut Rishap Dhakal verfasserin aut Tina Henriksson verfasserin aut Pawan Kumar Singh verfasserin aut Aakash Chawade verfasserin aut In Frontiers in Plant Science Frontiers Media S.A., 2011 13(2022) (DE-627)662359240 (DE-600)2613694-6 1664462X nnns volume:13 year:2022 https://doi.org/10.3389/fpls.2022.1010249 kostenfrei https://doaj.org/article/692c4eed62da40ce9d248770859da942 kostenfrei https://www.frontiersin.org/articles/10.3389/fpls.2022.1010249/full kostenfrei https://doaj.org/toc/1664-462X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 13 2022
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callnumber-first	S - Agriculture
author	Fernanda Leiva
spellingShingle	Fernanda Leiva misc SB1-1110 misc Fusarium head blight misc seed phenotyping misc seed morphological characters misc wheat misc visual scores misc SmartGrain misc Plant culture Phenotyping Fusarium head blight through seed morphology characteristics using RGB imaging
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topic_title	SB1-1110 Phenotyping Fusarium head blight through seed morphology characteristics using RGB imaging Fusarium head blight seed phenotyping seed morphological characters wheat visual scores SmartGrain
topic	misc SB1-1110 misc Fusarium head blight misc seed phenotyping misc seed morphological characters misc wheat misc visual scores misc SmartGrain misc Plant culture
topic_unstemmed	misc SB1-1110 misc Fusarium head blight misc seed phenotyping misc seed morphological characters misc wheat misc visual scores misc SmartGrain misc Plant culture
topic_browse	misc SB1-1110 misc Fusarium head blight misc seed phenotyping misc seed morphological characters misc wheat misc visual scores misc SmartGrain misc Plant culture
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title	Phenotyping Fusarium head blight through seed morphology characteristics using RGB imaging
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title_full	Phenotyping Fusarium head blight through seed morphology characteristics using RGB imaging
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author_browse	Fernanda Leiva Mustafa Zakieh Marwan Alamrani Rishap Dhakal Tina Henriksson Pawan Kumar Singh Aakash Chawade
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title_sort	phenotyping fusarium head blight through seed morphology characteristics using rgb imaging
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title_auth	Phenotyping Fusarium head blight through seed morphology characteristics using RGB imaging
abstract	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.
abstractGer	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.
abstract_unstemmed	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.
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title_short	Phenotyping Fusarium head blight through seed morphology characteristics using RGB imaging
url	https://doi.org/10.3389/fpls.2022.1010249 https://doaj.org/article/692c4eed62da40ce9d248770859da942 https://www.frontiersin.org/articles/10.3389/fpls.2022.1010249/full https://doaj.org/toc/1664-462X
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up_date	2024-07-03T23:37:44.918Z
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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. 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Phenotyping Fusarium head blight through seed morphology characteristics using RGB imaging

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