Severity assessment in the Nicotiana tabacum-Xylella fastidiosa subsp. pauca pathosystem: design and interlaboratory validation of a standard area diagram set

Pereira, Willian Eduardo Lino; Andrade, Sheila Maria Pereira de; Ponte, Emerson M. Del; Esteves, Mariana Bossi; Canale, Maria Cristina; Takita, Marco Aurélio; Coletta-Filho, H. D.; Souza, Alessandra Alves de

doi:10.1007/s40858-020-00401-5

Cited by 13 publications

(14 citation statements)

References 55 publications

(87 reference statements)

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“…2 The history of phytopathometry, 1892 to the present. Significant events and articles are indicated Bardsley and Ngugi 2013;Bock et al 2016b;Pereira et al 2020). The pivot around which these two phases occur is the recognition of a need for unifying methods of assessment to quantify crop loss in particular, including standardized methods allowing for reproducibility ("the extent to which two or more raters obtain the same estimates of disease severity of the same specimens", Madden et al 2007, also known as inter-rater reliability), recognized by the United Nations Food and Agriculture Organization in the mid-1960s (Chiarappa 1970), and culminating in publication of a crop loss assessment manual (Chiarappa 1971).…”

Section: The Rise Of Phytopathometrymentioning

confidence: 99%

“…Research on the topic has demonstrated that several aspects of the SADs design and evaluation might affect accuracy (and reliability) including rater experience (Yadav et al 2013), pathosystem (Godoy et al 1997), number of diagrams and structure and/or color of SADs (Schwanck and Del Ponte 2014;Bock et al 2015;Franceschi et al 2020), and the procedures followed during SAD development and validation, and other factors (Melo et al 2020;Pereira et al 2020). Franceschi et al (2020) demonstrated the substantial improvements that could be made with carefully designed SADs compared to older, basic, previously developed SADs (Fig.…”

Section: Establishment and Evolution Of Sads Researchmentioning

confidence: 99%

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Plant disease severity estimated visually: a century of research, best practices, and opportunities for improving methods and practices to maximize accuracy

Bock

Chiang

Ponte

2021

Trop. plant pathol.

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Plant disease quantification, mainly the intensity of disease symptoms on individual units (severity), is the basis for a plethora of research and applied purposes in plant pathology and related disciplines. These include evaluating treatment effect, monitoring epidemics, understanding yield loss, and phenotyping for host resistance. Although sensor technology has been available to measure disease severity using the visible spectrum or other spectral range imaging, it is visual sensing and perception that still dominates, especially in field research. Awareness of the importance of accuracy of visual estimates of severity began in 1892, when Cobb developed a set of diagrams as an aid to guide estimates of rust severity in wheat. Since that time, various approaches, some of them based on principles of psychophysics, have provided a foundation to understand sources of error during the estimation process as well as to develop different disease scales and disease-specific illustrations indicating the diseased area on specimens, similar to that developed by Cobb, and known as standard area diagrams (SADs). Several rater-related (experience, inherent ability, training) and technology-related (instruction, scales, and SADs) characteristics have been shown to affect accuracy. This review provides a historical perspective of visual severity assessment, accounting for concepts, tools, changing paradigms, and methods to maximize accuracy of estimates. A list of best-operating practices in plant disease quantification and future research on the topic is presented based on the current knowledge.

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Section: The Rise Of Phytopathometrymentioning

confidence: 99%

Section: Establishment and Evolution Of Sads Researchmentioning

confidence: 99%

Plant disease severity estimated visually: a century of research, best practices, and opportunities for improving methods and practices to maximize accuracy

Bock

Chiang

Ponte

2021

Trop. plant pathol.

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“…The history of phytopathometry as it relates to visual estimation of disease severity can be divided into two phases. A pre-1970 phase when there was no basis for assessing accuracy of severity estimates (Chester 1950;Large 1953 and1966), and the phase since 1970 during which there have been quantitative approaches to understanding error and improving accuracy and reliability ("the extent to which the same measurements of individuals [e.g., diseased specimens] obtained under different conditions yield similar results" Everitt, 1999) of estimates of severity (for example, Kranz, 1970;Forbes and Jeger 1987;Nutter et al 1993;Nutter and Schultz 1995;Nita et al 2003;Godoy et al 2006;Bardsley and Ngugi 2013;Bock et al 2016b;Pereira et al 2020). The pivot around which these two phases occur is the recognition of a need for unifying methods of assessment to quantify crop loss in particular, including standardized methods allowing for reproducibility, recognized by the United Nations Food and Agriculture Organization in the mid-1960s (Chiarappa 1970), and culminating in publication of a crop loss assessment manual (Chiarappa 1971).…”

Section: The Rise Of Phytopathometrymentioning

confidence: 99%

“…Spolti et al (2011) were the first to apply them to the study of SADs. Second, the use of statistical approaches other than regression analysis to explore accuracy including (ordered by first use) confidence interval (Spolti et al 2001) equivalence tests (Yadav et al 2013) Research on the topic has demonstrated that several aspects of the SADs design and evaluation might affect accuracy (and reliability) including rater experience (Yadav et al 2013), pathosystem (Godoy et al 1997), number of diagrams and structure and/or color of SADs (Schwanck and Del Ponte 2014;Bock et al 2015;Franceschi et al 2020), and the procedures followed during SAD development and validation, and other factors (Melo et al 2020;Pereira et al 2020). Franceschi et al (2020) demonstrated the substantial improvements that could be made with a carefully designed SADs compared to an older, basic, previously developed SADs (Figure 4) -raters' estimates were significantly more accurate with the new SADs.…”

Section: Establishment and Evolution Of Sads Researchmentioning

confidence: 99%

“…Chiang et al (2017a and b) Simulation was used to demonstrate optimization of the disease severity index for hypothesis testing. Melo et al (2020) Showed that SADs developed in different laboratories had different characteristics when validated (Pereira et al (2020) showed similar). Chiang et al (2020) Demonstrated use of the proportional odds model for analyzing ordinal scale data.…”

Section: The Future Of Visual Plant Disease Severity Estimationmentioning

confidence: 99%

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Plant disease severity estimated visually: a century of research, best practices and opportunities for improving methods and practices to maximize accuracy

Bock¹,

Chiang²,

Ponte³

2021

Preprint

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Plant disease quantification, mainly the intensity of disease symptoms on individual units (severity) is the basis for a plethora of research and applied purposes in plant pathology and related disciplines. These include evaluating treatment effect, monitoring epidemics, understanding yield loss, and phenotyping for host resistance. Although sensor technology has been available to measure disease severity using the visible spectrum or other spectral range imaging, it is visual sensing and perception that still dominates, especially in field research. Awareness of the importance of accuracy of visual estimates of severity began in 1892, when Cobb developed a set of diagrams as an aid to guide estimates of rust severity in wheat. Since that time, various approaches, some of them based on principles of psychophysics, have provided a foundation to understand sources of error during the estimation process as well as to develop different disease scales and disease-specific illustrations indicating the diseased area on specimens, similar to that developed by Cobb, and known as standard area diagrams (SADs). Several rater-related (experience, inherent ability, training) and technology-related (instruction, scales and SADs) characteristics have been shown to affect accuracy. This review provides a historical perspective of visual severity assessment, accounting for concepts, tools, changing paradigms, and methods to maximize accuracy of estimates. A list of best operating practices in plant disease quantification and future research on the topic is presented based on the current knowledge.

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RGB-based phenotyping of foliar disease severity under controlled conditions

Alves

Guimarães

Ascari

et al. 2021

Trop. plant pathol.

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Plant diseases induce visible modifications on leaves with the advance of infection and colonization, thus altering their spectral reflectance pattern. In this study, we evaluated the visible spectral region of symptomatic leaves of five plant diseases: soybean rust (SBR), calonectria leaf blight (CLB), wheat leaf blast (WLB), Nicotiana tabacum-Xylella fastidiosa (NtXf), and potato late blight (PLB). Ten spectral indices were calculated from the RGB channels (red, green, and blue) of images of leaves varying in percent severity, which were obtained under controlled lighting and homogeneous background. Image processing was automated for background removal and pixel-level index calculation. Each index was averaged across pixels at the leaf level. We found high levels of correlation between leaf severity and the majority of the spectral indices. The most highly associated spectral indices were HUE, VARI, NGRDI, HI, and SCI. The leaf-level mean value of each of the ten indices and digital numbers on the RGB channels were gathered and used to train boosted regression trees models for predicting the leaf severity of each disease. Models for SBR, CLB, and WLB achieved high prediction accuracies (>97%) on the testing dataset (20% of the original dataset). Models for NtXf and PLB had prediction accuracies below 90%. The performance of each model may be directly related to the symptomatology of each disease. The method can be automated if the images are obtained under controlled light and homogeneous background, but improvements should be made in the method for using fieldor greenhouse-acquired images which would require similar conditions.

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Severity assessment in the Nicotiana tabacum-Xylella fastidiosa subsp. pauca pathosystem: design and interlaboratory validation of a standard area diagram set

Cited by 13 publications

References 55 publications

Plant disease severity estimated visually: a century of research, best practices, and opportunities for improving methods and practices to maximize accuracy

Plant disease severity estimated visually: a century of research, best practices, and opportunities for improving methods and practices to maximize accuracy

Plant disease severity estimated visually: a century of research, best practices and opportunities for improving methods and practices to maximize accuracy

RGB-based phenotyping of foliar disease severity under controlled conditions

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