Skip to main content
Log in

A standard area diagram set for severity assessment of eyespot on rice

  • Research Note
  • Published:
Australasian Plant Pathology Aims and scope Submit manuscript

Abstract

This study aimed to develop and validate a standard area diagram set (SADs) to estimate the severity of eyespot of rice caused by Drechslera gigantea. For this purpose, a SADs with seven levels of severity (0.3; 1.0; 3.0; 5.0; 10; 15; and 21%) was established. The SADs was validated by 16 raters with no experience in evaluating plant diseases. Both accuracy and precision improved when they used the SADs. The statistical parameters for the 16 raters were: bias coefficient factor - Cb (no SADs = 0.404, with SADs = 0.994); correlation coefficient - r (no SADs = 0.884, with SADs = 0.953); and Lin’s concordance correlation coefficient - ρc (no SADs = 0.356, with SADs = 0.947). In addition, estimates were more reliable: inter-rater coefficient of determination - R2 (no SADs = 0.712, with SAD = 0.873); intra-class correlation coefficient - ρ (no SADs = 0.723, with SADs = 0.924). The SADs proposed here is a useful tool for improving visual assessments of eyespot severity of rice.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3

References

  • Ahn SW (1980) Eyespot of rice in Colombia, Panama, and Peru. Plant Dis 64:878–880

    Article  Google Scholar 

  • Bock CH, Poole GH, Parker PE, Gottwald TR (2010) Plant disease severity estimated visually, by digital photography and image analysis, and by hyperspectral imaging. Crit Ver Plant Sci 29:59–107

    Article  Google Scholar 

  • Del Ponte EM, Pethybridge SJ, Bock CH, Michereff SJ, Machado FJ, Spolti P (2017) Standard area diagrams for aiding severity estimation: scientometrics, pathosystems, and methodological trends in the last 25 years. Phytopathology 107:1161–1174

    PubMed  Google Scholar 

  • Dolinski MA, Duarte HSS, Silva JB, May de Mio LL (2017) Development and validation of a standard area diagram set for assessment of peach rust. Eur J Plant Pathol 148:817–824

    Article  Google Scholar 

  • Gamer M, Lemon J, Fellows I (2012) Irr: various coefficients of interrater reliability and agreement. R package version 0.84

  • Kardin MK, Bowden RL, Percich JA, Nickelson LJ (1982) Zonate eyespot on wild rice caused by Drechslera gigantea. Plant Dis 66:737–739

    Article  Google Scholar 

  • Ito S (1930) One some new ascigerous stages of the species of Helminthosporium parasitic on cereals. Proc Imper Acad 6:352–355

    Article  Google Scholar 

  • Lage DAC, Marouelli WA, Duarte HSS, Café-Filho AC (2015) Standard area diagrams for assessment of powdery mildew severity on tomato leaves and leaflets. Crop Prot 67:26–34

    Article  Google Scholar 

  • Nunes CDM (2008) Ocorrência das doenças: mal-do-pé (Gaeumannomyces graminis) e mancha-parda (Drechslera sp.) na cultura do arroz. Embrapa CPATB, circular técnica 205

  • Nunes CDM (2013) Doenças da cultura do arroz irrigado, processo da produção integrada. 1. Embrapa, Pelotas

  • Nuñez AMP, Monteiro FP, Pacheco LP, Rodríguez GAA, Nogueira CCA, Pinto FAMF, Medeiros FHV, Souza JT (2017) Development and validation of a diagrammatic scale to assess the severity of black rot of crucifers in kale. J Phytopathol 165:195–203

    Article  Google Scholar 

  • Nutter FW Jr, Esker PD (2006) The role of psychophysics in phytopathology: the weber–Fechner law revisited. Eur J Plant Pathol 114:199–213

    Article  Google Scholar 

  • Pethybridge SJ, Nelson SC (2015) Leaf doctor: a new portable application for quantifying plant disease severity. Plant Dis 99:1310–1316

    Article  Google Scholar 

  • R Core Team. (2020). R: a language and environment for statistical computing. Vienna: R Foundation for Statistical Computing. http://www.r-project.org/

  • Schwanck AA, Del Ponte EM (2014) Accuracy and reliability of severity estimates using linear or logarithmic disease diagram sets in true colour or black and white: a study case for rice brown spot. J Phytopathol 162:670–682

    Article  Google Scholar 

  • Stevenson, M. (2012). Epir: an R package for the analysis of epidemiological data. R package version 0.9–43

  • Singh VK, Sing A et al (2012) Incorporation of blast resistance into ‘PRR78’, an elite basmati rice restorer line, through marker assisted backcross breeding. Field Crops Res 128:8–16

    Article  Google Scholar 

  • Vale FXR, Fernandes Filho EI, Liberato JR (2003) QUANT: a software plant disease severity assessment. In: Close R, Braithwaite M, Havery I, eds. Proceedings of the 8th international congress of plant pathology, New Zealand. Sydney, NSW, Australia: Horticulture Australia, 105

Download references

Acknowledgments

The authors are thankful to the raters for their time assessing the diseased leaf images. The author also acknowledges the CAPES and PAEC OEA-GCUB - (OEA) for the financial support and for the student scholarships. LJ Dallagnol (grant number 308149/2018-1) and HSS Duarte (grant number 307297/2018-7) are supported by fellowships from Brazilian National Council for Scientific and Technological Development (CNPq).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Leandro J. Dallagnol.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Rivera, J.F., Duarte, H.S.S., Furtado, E.B. et al. A standard area diagram set for severity assessment of eyespot on rice. Australasian Plant Pathol. 49, 367–371 (2020). https://doi.org/10.1007/s13313-020-00709-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13313-020-00709-5

Keywords

Navigation