Quantification of Foliar Plant Disease Symptoms by Microcomputer-Digitized Video Image Analysis

Lindow, Steven E.

doi:10.1094/phyto-73-520

Cited by 81 publications

(54 citation statements)

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“…Regression analysis is the most widely used tool for testing the quality of disease severity measured using image analysis and other techniques. It has been used to judge reproducibility, reliability, precision and accuracy [32,40,41,[44][45][46][47][48][49]. It is a useful method, but it should be applied cautiously as incorrect conclusions might be drawn from the analysis under certain, circumstances [23,38].…”

Section: Statistical Methods To Gauge the Ability Of Image Analysis Imentioning

confidence: 99%

“…Statistically, the coefficient of determination describes the proportion of variability accounted for by the regression model (a measure of the proportion of overlapping variance). Accuracy and precision of measurements of disease using image analysis have been analysed using regression analysis for several diseases for both colour and monochrome images [32,34,40,41,45], but fewer studies have addressed repeatability and reproducibility of image analysis measurements. Results by Nutter et al [32] suggested poor repeatability (r 2 =0.63).…”

Section: Statistical Methods To Gauge the Ability Of Image Analysis Imentioning

confidence: 99%

“…For true values of disease severity on a particular leaf image, the leaf can be weighed before and after physically cutting out the diseased area [31,33,40], using planimeters [41][42][43] and by image analysis either directly using original images or enhanced images (e.g. outlined) by attempting to accurately define what are the diseased and healthy portions of the leaf or quadrat on an image-by-image basis [32,[44][45][46].…”

Section: Historical Background and The Terms Used In Plant Disease Asmentioning

confidence: 99%

“…Visual confirmation of disease can be used to judge this error in detection. Quantification of disease severity caused by Alternaria solani on tomato and Aschochyta pteridium on bracken fern was achieved using algorithms to correct variation in the image background, which resulted in an exceptional degree of accuracy (r 2 =0.99) with an intra-method reliability of +0.8% [41,71]. Martin and Rybicki [40] used algorithms and a step-wedge or photographic greyscale standard for calibration to allow automation of the image analysis system, enabling the identification of host resistance to maize streak virus in maize, with results superior to visual estimates [72].…”

Section: Delineating Disease On Digital Imagesmentioning

confidence: 99%

“…There are fewer recent reports of visible-wavelength photography solely being used to detect and quantify [41] http://www.cabi.org/cabreviews disease at the field or landscape scales. Johnson and Wear [116] detected conifer forest stand openings caused by Poria weirii root rot in Oregon using black and white aerial photography; however, accurately identifying what was the cause of the openings was not always possible.…”

Section: Application Of Visible-wavelength Photography and Image Analmentioning

confidence: 99%

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Detection and measurement of plant disease symptoms using visible-wavelength photography and image analysis.

Bock¹,

Nutter²

2011

CABI Reviews

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Plant disease detection and severity assessment are required for many purposes, including predicting yield loss, monitoring and forecasting epidemics, judging host resistance and for studying fundamental biological host-pathogen processes. If assessments of disease severity are inaccurate and/or imprecise, incorrect conclusions might be drawn and incorrect actions taken. Image analysis based on digital images made using visible wavelengths is one of the several methods used to detect and quantify disease; it offers advantages compared with visual assessment or other methods. Over the last 30 years, major advances have been made to improve reliability, precision and accuracy of image analysis for detecting and measuring plant disease. Although the equipment and software continue to become more sophisticated, these technologies are also becoming easier to use. As a result, image analysis to measure plant disease is becoming increasingly widely used, and has now been applied in the study of numerous plant diseases. This review describes the history, technology and application of visible-wavelength photography and image analysis, and progress towards realizing the full potential of these systems in plant disease detection and assessment RightsWorks produced by employees of the U.S. Government as part of their official duties are not copyrighted within the U.S. The content of this document is not copyrighted. AbstractPlant disease detection and severity assessment are required for many purposes, including predicting yield loss, monitoring and forecasting epidemics, judging host resistance and for studying fundamental biological host-pathogen processes. If assessments of disease severity are inaccurate and/or imprecise, incorrect conclusions might be drawn and incorrect actions taken. Image analysis based on digital images made using visible wavelengths is one of the several methods used to detect and quantify disease; it offers advantages compared with visual assessment or other methods. Over the last 30 years, major advances have been made to improve reliability, precision and accuracy of image analysis for detecting and measuring plant disease. Although the equipment and software continue to become more sophisticated, these technologies are also becoming easier to use. As a result, image analysis to measure plant disease is becoming increasingly widely used, and has now been applied in the study of numerous plant diseases. This review describes the history, technology and application of visible-wavelength photography and image analysis, and progress towards realizing the full potential of these systems in plant disease detection and assessment.Keywords: Digital images, Disease incidence, Disease severity, Image analysis, Plant disease assessment, Rater error, Remote sensing, Visible spectrumReview Methodology: We used existing reviews (cited herein) as a source to locate many of the articles. More recent articles were located through online searches using CAB abstracts, Agricola and Web of Science. Keywor...

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Section: Statistical Methods To Gauge the Ability Of Image Analysis Imentioning

confidence: 99%

Section: Statistical Methods To Gauge the Ability Of Image Analysis Imentioning

confidence: 99%

Section: Historical Background and The Terms Used In Plant Disease Asmentioning

confidence: 99%

Section: Delineating Disease On Digital Imagesmentioning

confidence: 99%

Section: Application Of Visible-wavelength Photography and Image Analmentioning

confidence: 99%

See 3 more Smart Citations

Detection and measurement of plant disease symptoms using visible-wavelength photography and image analysis.

Bock¹,

Nutter²

2011

CABI Reviews

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Disease assessment and yield loss

Cooke

The Epidemiology of Plant Diseases

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Estimating Disease Severity and Incidence

Campbell¹,

Neher²

1994

Epidemiology and Management of Root Diseases

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Quantification of Foliar Plant Disease Symptoms by Microcomputer-Digitized Video Image Analysis

Cited by 81 publications

References 0 publications

Detection and measurement of plant disease symptoms using visible-wavelength photography and image analysis.

Detection and measurement of plant disease symptoms using visible-wavelength photography and image analysis.

Disease assessment and yield loss

Estimating Disease Severity and Incidence

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