Literature DB >> 18943866

Reliability and accuracy of visual estimation of phomopsis leaf blight of strawberry.

M Nita, M A Ellis, L V Madden.   

Abstract

ABSTRACT Six different individuals (raters) assessed the severity of Phomopsis leaf blight on strawberry leaflets in five experimental repetitions over 2 years by making a direct visual estimation of the percentage of diseased area of each leaflet or by using the Horsfall-Barratt (H-B) disease scale. Intra-rater and inter-rater reliability and accuracy were determined, and then the relationship between visually estimated severity values and actual severity values was evaluated. Agreement in estimated disease severity values between assessment times by the same raters (i.e., intra-rater reliability), and agreement in disease severity values among raters at a single assessment time (i.e., inter-rater reliability), were both high, with most correlation coefficients being greater than 0.85. The intra-class correlation for overall agreement among raters ranged from 0.80 to 0.96 for the five repetitions. Based on the concordance coefficient calculated for each rater in each repetition, agreement between estimated and actual severity (i.e., accuracy) was somewhat lower than reliability. The relationship between estimated and actual severity was linear, and there was a slight trend to overestimate disease severity. The H-B scale was not more reliable or accurate than direct estimation of severity, and the linear relationship between estimated and actual severity did not support the principles underling the H-B scale. Both size of leaflets and number of lesions per leaflet slightly affected the error in estimate of disease severity.

Entities:  

Year:  2003        PMID: 18943866     DOI: 10.1094/PHYTO.2003.93.8.995

Source DB:  PubMed          Journal:  Phytopathology        ISSN: 0031-949X            Impact factor:   4.025


  4 in total

1.  Accuracy, reliability, and timing of visual evaluations of decay in fresh-cut lettuce.

Authors:  Ivan Simko; Ryan J Hayes
Journal:  PLoS One       Date:  2018-04-17       Impact factor: 3.240

2.  Wheat Spike Blast Image Classification Using Deep Convolutional Neural Networks.

Authors:  Mariela Fernández-Campos; Yu-Ting Huang; Mohammad R Jahanshahi; Tao Wang; Jian Jin; Darcy E P Telenko; Carlos Góngora-Canul; C D Cruz
Journal:  Front Plant Sci       Date:  2021-06-17       Impact factor: 5.753

Review 3.  Understanding the ramifications of quantitative ordinal scales on accuracy of estimates of disease severity and data analysis in plant pathology.

Authors:  Kuo-Szu Chiang; Clive H Bock
Journal:  Trop Plant Pathol       Date:  2021-07-13       Impact factor: 2.404

4.  Rapid, automated detection of stem canker symptoms in woody perennials using artificial neural network analysis.

Authors:  Bo Li; Michelle T Hulin; Philip Brain; John W Mansfield; Robert W Jackson; Richard J Harrison
Journal:  Plant Methods       Date:  2015-12-24       Impact factor: 4.993

  4 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.