Plant Disease Severity Estimated Visually, by Digital Photography and Image Analysis, and by Hyperspectral Imaging

Abstract

Reliable, precise and accurate estimates of disease severity are important for predicting yield loss, monitoring and forecasting epidemics, for assessing crop germplasm for disease resistance, and for understanding fundamental biological processes including co-evolution. Disease assessments that are inaccurate and/or imprecise might lead to faulty conclusions being drawn from the data, which in turn can lead to incorrect actions being taken in disease management decisions. Plant disease can be quantified in several different ways. This review considers plant disease severity assessment at the scale of individual plant parts or plants, and describes our current understanding of the sources and causes of assessment error, a better understanding of which is required before improvements can be targeted. The review also considers how these can be identified using various statistical tools. Indeed, great strides have been made in the last thirty years in identifying the sources of assessment error inherent to visual rating, and this review highlights ways that assessment errors can be reduced—particularly by training raters or using assessment aids. Lesion number in relation to area infected is known to influence accuracy and precision of visual estimates—the greater the number of lesions for a given area infected results in more overestimation. Furthermore, there is a widespread tendency to overestimate disease severity at low severities (<10%). Both interrater and intrarater reliability can be variable, particularly if training or rating aids are not used. During the last eighty years acceptable accuracy and precision of visual disease assessments have often been achieved using disease scales, particularly because of the time they allegedly save, and the ease with which they can be learned, but recent work suggests there can be some disadvantages to their use. This review considers new technologies that offer opportunity to assess disease with greater objectivity (reliability, precision, and accuracy). One of these, visible light photography and digital image analysis has been increasingly used over the last thirty years, as software has become more sophisticated and user-friendly. Indeed, some studies have produced very accurate estimates of disease using image analysis. In contrast, hyperspectral imagery is relatively recent and has not been widely applied in plant pathology. Nonetheless, it offers interesting and potentially discerning opportunities to assess disease. As plant disease assessment becomes better understood, it is against the backdrop of concepts of reliability, precision and accuracy (and agreement) in plant pathology and measurement science. This review briefly describes these concepts in relation to plant disease assessment. Various advantages and disadvantages of the different approaches to disease assessment are described. For each assessment method some future research priorities are identified that would be of value in better understanding the theory of disease assessment, as it applies to improving and fully realizing the potential of image analysis and hyperspectral imagery.

Keywords:

• plant disease assessment
• variance
• error
• image analysis
• hyperspectral imagery
• remote sensing

ACKNOWLEDGMENTS

We appreciate the time, useful comments and helpful suggestions of several reviewers in reading draft versions of this review. Professor Forrest W. Nutter was also extremely generous with his time reviewing the manuscript and making numerous suggestions that have improved the content and readability – we are particularly grateful for his input. Ute Albrecht is thanked for help with the German translation of various documents. We also appreciate permission from different sources to reproduce figures that were used to illustrate specific points in the article.

Notes

*Dr. Bock was formerly associated with the University of Florida/USDA, Ft. Pierce, FL, USA, at the time this article was written.

**This article is not subject to U.S. copyright law.