Development and validation of a set of standard area diagrams to aid in estimation of spot blotch severity on wheat leaves

Domiciano, Gisele Pereira; Duarte, Henrique da Silva Silveira; Moreira, E. N.; Rodrigues, Fabrício Ávila

doi:10.1111/ppa.12150

Cited by 29 publications

(15 citation statements)

References 25 publications

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“…Overall, the use of SADs helps standardize raters, improving inter-rater reliability (itself a result of the accuracy of estimates of severity on individual specimens). Agreement (ρ c ) on the 0 to 100% range with actual values from image analysis frequently > 0.90 when using SADs (Spolti et al 2011;Duarte et al 2013;Domiciano et al 2014;González-Domínguez et al 2014). This can be considered excellent agreement in measurement science (Altman 1991), although others are more conservative (McBride 2005).…”

Section: Methods To Improve Accuracy Of Estimatesmentioning

confidence: 88%

From visual estimates to fully automated sensor-based measurements of plant disease severity: status and challenges for improving accuracy

et al. 2020

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The severity of plant diseases, traditionally the proportion of the plant tissue exhibiting symptoms, is a key quantitative variable to know for many diseases and is prone to error. Good quality disease severity data should be accurate (close to the true value). Earliest quantification of disease severity was by visual estimates. Sensor-based image analysis including visible spectrum and hyperspectral and multispectral sensors are established technologies that promise to substitute, or complement visual ratings. Indeed, these technologies have measured disease severity accurately under controlled conditions but are yet to demonstrate their full potential for accurate measurement under field conditions. Sensor technology is advancing rapidly, and artificial intelligence may help overcome issues for automating severity measurement under hyper-variable field conditions. The adoption of appropriate scales, training, instruction and aids (standard area diagrams) has contributed to improved accuracy of visual estimates. The apogee of accuracy for visual estimation is likely being approached, and any remaining increases in accuracy are likely to be small. Due to automation and rapidity, sensor-based measurement offers potential advantages compared with visual estimates, but the latter will remain important for years to come. Mobile, automated sensor-based systems will become increasingly common in controlled conditions and, eventually, in the field for measuring plant disease severity for the purpose of research and decision making.

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Section: Methods To Improve Accuracy Of Estimatesmentioning

confidence: 88%

From visual estimates to fully automated sensor-based measurements of plant disease severity: status and challenges for improving accuracy

et al. 2020

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“…Consequently, estimates made without SADs had small bias, and were relatively precise, accurate and reliable, with only a few exceptions. Other studies have demonstrated that SADs are effective aids, particularly for inexperienced raters (Nutter et al ., ; Nutter & Esker, ; Godoy et al ., ; Domiciano et al ., ; Yadav et al ., ; Schwanck & Del Ponte, ). Significant improvement in bias, accuracy, precision and inter‐rater reliability showed some trends but were not entirely consistent.…”

Section: Discussionmentioning

confidence: 98%

Assessing disease severity: accuracy and reliability of rater estimates in relation to number of diagrams in a standard area diagram set

Bock¹,

Hotchkiss²,

Wood³

2015

Plant Pathology

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Error in estimates of plant disease severity occur and standard area diagrams (SADs) help improve accuracy and reliability. The effects of diagram number in SADs are unknown. The objective of this study was to compare estimates of pecan scab severity made without SADs, and using three-, five-, seven-or 10-diagram SADs. Disease severity was estimated to the nearest percent (NPE), or classified to the closest reference diagram value using a scale. Twelve raters assessed 20 images of scabbed pecan valves with and without the SADs using NPEs and the scale method (values were converted to midpoints prior to analysis). Increases in diagram number using NPEs did not necessarily result in more accurate or reliable estimates. Inter-rater reliability was positively correlated with number of diagrams using NPEs (r = 0Á3288 (P < 0Á0001)) or a scale (r = 0Á2803 (P < 0Á0001)). The least accurate estimates improved the most using SADs with NPEs, but the gain did not relate to number of diagrams; as few as three diagrams reduced error of inaccurate estimates as much as five, seven or 10 diagrams. When used as a scale, only estimates made with 10-diagram SADs had similar accuracy and inter-rater reliability to estimates made using SADs with NPEs. Maximum disease severity and the disease severity range for the pathosystem and the SADs are important factors and will probably influence resulting accuracy and reliability. The ramifications of diagram numbers in SADs and assessment methods on accuracy and reliability of disease estimates are discussed.

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“…Similar results were found in previous studies conducted by several researchers (ALVES et al, 2015;CAPUCHO et al, 2011;CUSTÓDIO ET AL., 2011;DOMICIANO et al, 2013;DUARTE et al, 2013;JULIATTI et al, 2013;LENZ et al, 2010;LIMA et al, 2013;NUNES;ALVES, 2012;REIS, CASA;BRESOLIN., 2013;SOUSA et al, 2014;TERAMOTO et al, 2011;TROJAN;PRIA, 2018), showing an increase in precision after using a diagrammatic scale, demonstrating the importance of this tool in quantifying disease severity.…”

Section: Evaluatormentioning

confidence: 99%

Development and validation of a diagrammatic scale for quantifying maize leaf spots caused by Diplodia macrospora

Lorenzetti

Tartaro

Neto

et al. 2019

SCA

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The objective of this study was to develop and validate a diagrammatic scale to evaluate the severity of spots on maize leaves caused by the fungus Diplodia macrospora. Severity ranged between the minimal (0.5%) and maximal (55%) limits of disease severity, and intermediate severity levels were defined according to the "Weber-Fechner stimulus response law". The proposed scale describes six levels of severity based on how much of the leaf is affected: 0.5%, 3%, 8%, 23%, 36%, and 55%. Validation was carried out by eight evaluators, four inexperienced and four experienced. They estimated the severity of disease in 60 maize leaves, with and without the proposed diagrammatic scale. A relationship was shown by regression analysis between estimated and actual severity, with and without the use of the scale. When both inexperienced and experienced evaluators used the scale, they were able to estimate disease severity more accurately and precisely.

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Development and validation of a set of standard area diagrams to aid in estimation of spot blotch severity on wheat leaves

Cited by 29 publications

References 25 publications

From visual estimates to fully automated sensor-based measurements of plant disease severity: status and challenges for improving accuracy

From visual estimates to fully automated sensor-based measurements of plant disease severity: status and challenges for improving accuracy

Assessing disease severity: accuracy and reliability of rater estimates in relation to number of diagrams in a standard area diagram set

Development and validation of a diagrammatic scale for quantifying maize leaf spots caused by Diplodia macrospora

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