Optimizing visual estimation of peanut late leaf spot severity with online training sessions and standard area diagrams

Autores
Cazon, Luis Ignacio; Paredes, Juan Andrés; González, N.R.; Conforto, Erica Cinthia; Suarez, L.; Del Ponte, Emerson M.
Año de publicación
2025
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Quantification of plant disease severity is key for plant pathology research, particularly in the evaluation of disease management strategies. Visual estimation of severity remains widely used, especially in field experiments. Training sessions and the use of standard area diagram sets (SADs) are known to enhance rater accuracy. In this study, we aimed to quantify and compare the benefits of these tools, either used alone or in combination, when visually assessing peanut late leaf spot severity. We designed and validated SADs to aid in disease severity estimation and also evaluated the training tool TraineR2, a web-based app that contains actual images of the disease with known severity. Our results show that both tools led to a significant improvement in rater accuracy after their use. For TraineR2, the gains in overall accuracy (ρc from 0.82 to 0.91) and precision (Pearson's r from 0.73 to 0.88) were slightly lower than those obtained with the SADs (ρc from 0.89 to 0.96 and Pearson's r from 0.85 to 0.95). When training and SADs were combined, the overall accuracy was 0.97, and Pearson's r was 0.96, values statistically similar to those achieved using SADs alone. Regarding inter-rater reliability, evaluated based on the intraclass correlation coefficient (ICC), using SADs and training together resulted in an ICC of 0.95, which was higher than using SADs alone (0.93) or training alone (0.84). Our study confirms the utility of combining training sessions and SADs for improving the accuracy of plant disease assessments.
Instituto de Patología Vegetal
Fil: Cazon, Luis Ignacio. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Patología Vegetal; Argentina
Fil: Cazon, Luis Ignacio. Consejo Nacional de Investigaciones Científicas y Técnicas. Unidad de Fitopatología y Modelización Agrícola (UFyMA); Argentina
Fil: Paredes, Juan Andrés. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Patología Vegetal; Argentina
Fil: Paredes, Juan Andrés. Consejo Nacional de Investigaciones Científicas y Técnicas. Unidad de Fitopatología y Modelización Agrícola (UFyMA); Argentina
Fil: González, N.R. Fundación ArgenINTA. Delegación IFFIVE. Córdoba; Argentina
Fil: Conforto, Erica Cinthia. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Patología Vegetal; Argentina
Fil: Conforto, Erica Cinthia. Consejo Nacional de Investigaciones Científicas y Técnicas. Unidad de Fitopatología y Modelización Agrícola (UFyMA); Argentina
Fil: Suarez, L. Fundación ArgenINTA. Delegación IFFIVE. Córdoba; Argentina
Fil: Del Ponte, E. M. Universidade Federal de Viçosa. Departamento de Fitopatologia; Brasil
Fuente
European Journal of Plant Pathology : 1-15. (Published: 14 March 2025)
Materia
Arachis hypogaea
Enfermedades de las Plantas
Plant Diseases
Nothopassalora personata
Maní
TraineR2
Phytopathometry
Nivel de accesibilidad
acceso restringido
Condiciones de uso
http://creativecommons.org/licenses/by-nc-sa/4.0/
Repositorio
INTA Digital (INTA)
Institución
Instituto Nacional de Tecnología Agropecuaria
OAI Identificador
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oai_identifier_str oai:localhost:20.500.12123/21707
network_acronym_str INTADig
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spelling Optimizing visual estimation of peanut late leaf spot severity with online training sessions and standard area diagramsCazon, Luis IgnacioParedes, Juan AndrésGonzález, N.R.Conforto, Erica CinthiaSuarez, L.Del Ponte, Emerson M.Arachis hypogaeaEnfermedades de las PlantasPlant DiseasesNothopassalora personataManíTraineR2PhytopathometryQuantification of plant disease severity is key for plant pathology research, particularly in the evaluation of disease management strategies. Visual estimation of severity remains widely used, especially in field experiments. Training sessions and the use of standard area diagram sets (SADs) are known to enhance rater accuracy. In this study, we aimed to quantify and compare the benefits of these tools, either used alone or in combination, when visually assessing peanut late leaf spot severity. We designed and validated SADs to aid in disease severity estimation and also evaluated the training tool TraineR2, a web-based app that contains actual images of the disease with known severity. Our results show that both tools led to a significant improvement in rater accuracy after their use. For TraineR2, the gains in overall accuracy (ρc from 0.82 to 0.91) and precision (Pearson's r from 0.73 to 0.88) were slightly lower than those obtained with the SADs (ρc from 0.89 to 0.96 and Pearson's r from 0.85 to 0.95). When training and SADs were combined, the overall accuracy was 0.97, and Pearson's r was 0.96, values statistically similar to those achieved using SADs alone. Regarding inter-rater reliability, evaluated based on the intraclass correlation coefficient (ICC), using SADs and training together resulted in an ICC of 0.95, which was higher than using SADs alone (0.93) or training alone (0.84). Our study confirms the utility of combining training sessions and SADs for improving the accuracy of plant disease assessments.Instituto de Patología VegetalFil: Cazon, Luis Ignacio. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Patología Vegetal; ArgentinaFil: Cazon, Luis Ignacio. Consejo Nacional de Investigaciones Científicas y Técnicas. Unidad de Fitopatología y Modelización Agrícola (UFyMA); ArgentinaFil: Paredes, Juan Andrés. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Patología Vegetal; ArgentinaFil: Paredes, Juan Andrés. Consejo Nacional de Investigaciones Científicas y Técnicas. Unidad de Fitopatología y Modelización Agrícola (UFyMA); ArgentinaFil: González, N.R. Fundación ArgenINTA. Delegación IFFIVE. Córdoba; ArgentinaFil: Conforto, Erica Cinthia. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Patología Vegetal; ArgentinaFil: Conforto, Erica Cinthia. Consejo Nacional de Investigaciones Científicas y Técnicas. Unidad de Fitopatología y Modelización Agrícola (UFyMA); ArgentinaFil: Suarez, L. Fundación ArgenINTA. Delegación IFFIVE. Córdoba; ArgentinaFil: Del Ponte, E. M. Universidade Federal de Viçosa. Departamento de Fitopatologia; BrasilSpringer2025-03-18T16:01:54Z2025-03-18T16:01:54Z2025-03info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfhttp://hdl.handle.net/20.500.12123/21707https://link.springer.com/article/10.1007/s10658-025-03016-10929-18731573-8469 (online)https://doi.org/10.1007/s10658-025-03016-1European Journal of Plant Pathology : 1-15. (Published: 14 March 2025)reponame:INTA Digital (INTA)instname:Instituto Nacional de Tecnología Agropecuariaenginfo:eu-repo/semantics/restrictedAccesshttp://creativecommons.org/licenses/by-nc-sa/4.0/Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)2026-09-24T11:41:19Zoai:localhost:20.500.12123/21707instacron:INTAInstitucionalhttp://repositorio.inta.gob.ar/Organismo científico-tecnológicoNo correspondehttp://repositorio.inta.gob.ar/oai/requesttripaldi.nicolas@inta.gob.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:l2026-09-24 11:41:19.734INTA Digital (INTA) - Instituto Nacional de Tecnología Agropecuariafalse
dc.title.none.fl_str_mv Optimizing visual estimation of peanut late leaf spot severity with online training sessions and standard area diagrams
title Optimizing visual estimation of peanut late leaf spot severity with online training sessions and standard area diagrams
spellingShingle Optimizing visual estimation of peanut late leaf spot severity with online training sessions and standard area diagrams
Cazon, Luis Ignacio
Arachis hypogaea
Enfermedades de las Plantas
Plant Diseases
Nothopassalora personata
Maní
TraineR2
Phytopathometry
title_short Optimizing visual estimation of peanut late leaf spot severity with online training sessions and standard area diagrams
title_full Optimizing visual estimation of peanut late leaf spot severity with online training sessions and standard area diagrams
title_fullStr Optimizing visual estimation of peanut late leaf spot severity with online training sessions and standard area diagrams
title_full_unstemmed Optimizing visual estimation of peanut late leaf spot severity with online training sessions and standard area diagrams
title_sort Optimizing visual estimation of peanut late leaf spot severity with online training sessions and standard area diagrams
dc.creator.none.fl_str_mv Cazon, Luis Ignacio
Paredes, Juan Andrés
González, N.R.
Conforto, Erica Cinthia
Suarez, L.
Del Ponte, Emerson M.
author Cazon, Luis Ignacio
author_facet Cazon, Luis Ignacio
Paredes, Juan Andrés
González, N.R.
Conforto, Erica Cinthia
Suarez, L.
Del Ponte, Emerson M.
author_role author
author2 Paredes, Juan Andrés
González, N.R.
Conforto, Erica Cinthia
Suarez, L.
Del Ponte, Emerson M.
author2_role author
author
author
author
author
dc.subject.none.fl_str_mv Arachis hypogaea
Enfermedades de las Plantas
Plant Diseases
Nothopassalora personata
Maní
TraineR2
Phytopathometry
topic Arachis hypogaea
Enfermedades de las Plantas
Plant Diseases
Nothopassalora personata
Maní
TraineR2
Phytopathometry
dc.description.none.fl_txt_mv Quantification of plant disease severity is key for plant pathology research, particularly in the evaluation of disease management strategies. Visual estimation of severity remains widely used, especially in field experiments. Training sessions and the use of standard area diagram sets (SADs) are known to enhance rater accuracy. In this study, we aimed to quantify and compare the benefits of these tools, either used alone or in combination, when visually assessing peanut late leaf spot severity. We designed and validated SADs to aid in disease severity estimation and also evaluated the training tool TraineR2, a web-based app that contains actual images of the disease with known severity. Our results show that both tools led to a significant improvement in rater accuracy after their use. For TraineR2, the gains in overall accuracy (ρc from 0.82 to 0.91) and precision (Pearson's r from 0.73 to 0.88) were slightly lower than those obtained with the SADs (ρc from 0.89 to 0.96 and Pearson's r from 0.85 to 0.95). When training and SADs were combined, the overall accuracy was 0.97, and Pearson's r was 0.96, values statistically similar to those achieved using SADs alone. Regarding inter-rater reliability, evaluated based on the intraclass correlation coefficient (ICC), using SADs and training together resulted in an ICC of 0.95, which was higher than using SADs alone (0.93) or training alone (0.84). Our study confirms the utility of combining training sessions and SADs for improving the accuracy of plant disease assessments.
Instituto de Patología Vegetal
Fil: Cazon, Luis Ignacio. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Patología Vegetal; Argentina
Fil: Cazon, Luis Ignacio. Consejo Nacional de Investigaciones Científicas y Técnicas. Unidad de Fitopatología y Modelización Agrícola (UFyMA); Argentina
Fil: Paredes, Juan Andrés. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Patología Vegetal; Argentina
Fil: Paredes, Juan Andrés. Consejo Nacional de Investigaciones Científicas y Técnicas. Unidad de Fitopatología y Modelización Agrícola (UFyMA); Argentina
Fil: González, N.R. Fundación ArgenINTA. Delegación IFFIVE. Córdoba; Argentina
Fil: Conforto, Erica Cinthia. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Patología Vegetal; Argentina
Fil: Conforto, Erica Cinthia. Consejo Nacional de Investigaciones Científicas y Técnicas. Unidad de Fitopatología y Modelización Agrícola (UFyMA); Argentina
Fil: Suarez, L. Fundación ArgenINTA. Delegación IFFIVE. Córdoba; Argentina
Fil: Del Ponte, E. M. Universidade Federal de Viçosa. Departamento de Fitopatologia; Brasil
description Quantification of plant disease severity is key for plant pathology research, particularly in the evaluation of disease management strategies. Visual estimation of severity remains widely used, especially in field experiments. Training sessions and the use of standard area diagram sets (SADs) are known to enhance rater accuracy. In this study, we aimed to quantify and compare the benefits of these tools, either used alone or in combination, when visually assessing peanut late leaf spot severity. We designed and validated SADs to aid in disease severity estimation and also evaluated the training tool TraineR2, a web-based app that contains actual images of the disease with known severity. Our results show that both tools led to a significant improvement in rater accuracy after their use. For TraineR2, the gains in overall accuracy (ρc from 0.82 to 0.91) and precision (Pearson's r from 0.73 to 0.88) were slightly lower than those obtained with the SADs (ρc from 0.89 to 0.96 and Pearson's r from 0.85 to 0.95). When training and SADs were combined, the overall accuracy was 0.97, and Pearson's r was 0.96, values statistically similar to those achieved using SADs alone. Regarding inter-rater reliability, evaluated based on the intraclass correlation coefficient (ICC), using SADs and training together resulted in an ICC of 0.95, which was higher than using SADs alone (0.93) or training alone (0.84). Our study confirms the utility of combining training sessions and SADs for improving the accuracy of plant disease assessments.
publishDate 2025
dc.date.none.fl_str_mv 2025-03-18T16:01:54Z
2025-03-18T16:01:54Z
2025-03
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/20.500.12123/21707
https://link.springer.com/article/10.1007/s10658-025-03016-1
0929-1873
1573-8469 (online)
https://doi.org/10.1007/s10658-025-03016-1
url http://hdl.handle.net/20.500.12123/21707
https://link.springer.com/article/10.1007/s10658-025-03016-1
https://doi.org/10.1007/s10658-025-03016-1
identifier_str_mv 0929-1873
1573-8469 (online)
dc.language.none.fl_str_mv eng
language eng
dc.rights.none.fl_str_mv info:eu-repo/semantics/restrictedAccess
http://creativecommons.org/licenses/by-nc-sa/4.0/
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
eu_rights_str_mv restrictedAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-sa/4.0/
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Springer
publisher.none.fl_str_mv Springer
dc.source.none.fl_str_mv European Journal of Plant Pathology : 1-15. (Published: 14 March 2025)
reponame:INTA Digital (INTA)
instname:Instituto Nacional de Tecnología Agropecuaria
reponame_str INTA Digital (INTA)
collection INTA Digital (INTA)
instname_str Instituto Nacional de Tecnología Agropecuaria
repository.name.fl_str_mv INTA Digital (INTA) - Instituto Nacional de Tecnología Agropecuaria
repository.mail.fl_str_mv tripaldi.nicolas@inta.gob.ar
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