Development and validation of a standard area diagram set to aid assessment of severity of loquat scab on fruit

González‐Domínguez, Elisa; Martins, Ricardo Brainer; Ponte, Emerson M. Del; Michereff, Sami Jorge; García‐Jiménez, J.; Armengol, Josep

doi:10.1007/s10658-014-0400-2

Cited by 18 publications

(22 citation statements)

References 27 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: 87%

“…During the last 25 years, research on SAD development and validation has intensified, further demonstrating the value of SADs for improving accuracy (Del Ponte et al 2017). Gains using SADs are variable among raters and across pathosystems (Spolti et al 2011;Yadav et al 2013;Schwanck and Del Ponte 2014), and are generally greatest for those raters who are least accurate (Yadav et al 2013;Braido et al 2014;González-Domínguez et al 2014;Debona et al 2015;Duan et al 2015). Increase (Δ) in agreement (based on Lin's concordance correlation, ρ c ) may range from Δ > 0.4 for inexperienced raters, to Δ~0, or possibly a slight loss in agreement for innately accurate raters.…”

Section: Methods To Improve Accuracy Of Estimatesmentioning

confidence: 99%

“…Experience, general field-based training and other methods Experience in recognizing disease symptoms does have an impact on ability to estimate accurately. Although individual, inexperienced raters may be innately more accurate than some experienced raters, as a group, experienced raters tend to be more accurate (Yadav et al 2013;González-Domínguez et al 2014). Grids comprised of squares that overlay a leaf (or other specimen area) were shown to improve accuracy (Parker et al 1995b) but have never been widely implemented.…”

Section: Methods To Improve Accuracy Of Estimatesmentioning

confidence: 99%

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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: 87%

Section: Methods To Improve Accuracy Of Estimatesmentioning

confidence: 99%

Section: Methods To Improve Accuracy Of Estimatesmentioning

confidence: 99%

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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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“…Based on the available information (Prota, 1960;Salerno, 1971a;Ptskialadze, 1968;González-Domínguez et al, 2013, 2014b, 2014dSánchez-Torres et al, 2009), the life cycle of F. eriobotryae under the Mediterranean climate is described in Fig. 6.1.…”

Section: Model Developmentmentioning

confidence: 99%

“…Although loquat scab is a well-known problem in the areas where loquat trees are cultivated, the biology of F. eriobotryae and the epidemiology of the disease have been seldom studied (Prota, 1960;Salerno, 1971a;Ptskialadze, 1968;González-Domínguez et al, 2013b, 2014b, 2014dSánchez-Torres et al, 2009). These studies have depicted F. eriobotryae as a highly rain-dependent pathogen that requires mild temperatures and long wet periods to infect loquat trees.…”

Section: Introductionmentioning

confidence: 99%

Biology, epidemiology and control of Fusicladium eriobotryae, causal agent of loquat scab

Domínguez¹

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Scab caused by the fungus Fusicladium eriobotryae is the most serious disease affecting loquat in Spain. Isolation of F. eriobotryae from infected tissue on culture media can be difficult due to its slow growth. A polymerase chain reaction (PCR) based protocol was developed for F. eriobotryae-specific identification from pure culture or infected loquat tissues. The primer set was designed in the elongation factor 1-α gene (EF1-α), and specificity and sensitivity for single and nested-PCR were validated. The nested-PCR assay resulted in 100% positive detection of F. eriobotryae in naturally and artificially infected tissues. This protocol can be useful for routine diagnosis, disease monitoring programs and epidemiological research.

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Standard area diagrams to assess black crust (Phyllachora huberi) severity on rubber tree leaflets

et al. 2020

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Development and validation of a standard area diagram set to aid assessment of severity of loquat scab on fruit

Cited by 18 publications

References 27 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

Biology, epidemiology and control of Fusicladium eriobotryae, causal agent of loquat scab

Standard area diagrams to assess black crust (Phyllachora huberi) severity on rubber tree leaflets

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