Optimizing visual estimation of peanut late leaf spot severity with online training sessions and standard area diagrams
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 kn...
| Main Authors: | , , , , , |
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| Format: | Artículo |
| Language: | Inglés |
| Published: |
Springer
2025
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| Online Access: | 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 |
| _version_ | 1855486805995945984 |
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| author | Cazon, Luis Ignacio Paredes, Juan Andrés González, N.R. Conforto, Erica Cinthia Suarez, L. Del Ponte, Emerson M. |
| author_browse | Cazon, Luis Ignacio Conforto, Erica Cinthia Del Ponte, Emerson M. González, N.R. Paredes, Juan Andrés Suarez, L. |
| author_facet | Cazon, Luis Ignacio Paredes, Juan Andrés González, N.R. Conforto, Erica Cinthia Suarez, L. Del Ponte, Emerson M. |
| author_sort | Cazon, Luis Ignacio |
| collection | INTA Digital |
| 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. |
| format | Artículo |
| id | INTA21707 |
| institution | Instituto Nacional de Tecnología Agropecuaria (INTA -Argentina) |
| language | Inglés |
| publishDate | 2025 |
| publishDateRange | 2025 |
| publishDateSort | 2025 |
| publisher | Springer |
| publisherStr | Springer |
| record_format | dspace |
| spelling | INTA217072025-03-18T16:16:22Z Optimizing visual estimation of peanut late leaf spot severity with online training sessions and standard area diagrams Cazon, Luis Ignacio Paredes, Juan Andrés González, N.R. Conforto, Erica Cinthia Suarez, L. Del Ponte, Emerson M. Arachis hypogaea Enfermedades de las Plantas Plant Diseases Nothopassalora personata Maní TraineR2 Phytopathometry 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 2025-03-18T16:01:54Z 2025-03-18T16:01:54Z 2025-03 info:ar-repo/semantics/artículo info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion 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 eng 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) application/pdf Springer European Journal of Plant Pathology : 1-15. (Published: 14 March 2025) |
| spellingShingle | Arachis hypogaea Enfermedades de las Plantas Plant Diseases Nothopassalora personata Maní TraineR2 Phytopathometry Cazon, Luis Ignacio Paredes, Juan Andrés González, N.R. Conforto, Erica Cinthia Suarez, L. Del Ponte, Emerson M. 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 |
| 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_short | 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 |
| topic | Arachis hypogaea Enfermedades de las Plantas Plant Diseases Nothopassalora personata Maní TraineR2 Phytopathometry |
| 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 |
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