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ARS Home » Southeast Area » Fort Pierce, Florida » U.S. Horticultural Research Laboratory » Subtropical Plant Pathology Research » Research » Publications at this Location » Publication #243057

Title: Estimation of plant disease severity visually, by digital photography and image analysis, and by hyperspectral imaging

item BOCK, CLIVE - University Of Florida
item Poole, Gavin
item PARKER, PAUL - Animal And Plant Health Inspection Service (APHIS)
item Gottwald, Timothy

Submitted to: Critical Reviews in Plant Sciences
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 11/1/2009
Publication Date: 2/1/2010
Citation: Bock, C., Poole, G.H., Parker, P., Gottwald, T.R. 2010. Estimation of plant disease severity visually, by digital photography and image analysis, and by hyperspectral imaging. Critical Reviews in Plant Sciences. 29:59-107

Interpretive Summary: This is an in depth review article of the state of technology for the estimation of disease severity in plants. All of the common techniques are reviewed, including visual assesment, digital photography, and hyperspectral analysis, as well as many different image analysis techniques.

Technical 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. In some situations poor quality assessment might increase the risk of collecting poor data which will result in faulty analysis and consequently incorrect actions or conclusions. Plant disease can be quantified in several different ways. This review concentrates on plant disease severity assessment at the scale of individual plant parts or plants, and describes our current understanding of the sources and causes of error, an understanding of which is required before improvements can be targeted. The review also considers how these can be identified using various statistical tools. Indeed, in the last thirty years great strides have been made identifying the sources of error inherent to visual rating, and the review highlights ways they can be reduced – particularly by training raters or using assessment aids. Lesion number in relation to area infected is known to influence the quality of the estimate – the greater number of lesions for a given area infected results in more overestimation. Furthermore, there is a widespread tendency to overestimate disease at low disease severity (<10%). Both inter-rater and intra-rater reliability can be variable, particularly so if training or rating aids are not used. During the last eighty years visual disease assessment has often been achieved using disease scales, particularly because of the time they allegedly save, and the ease with which they can be learnt, but recent work suggests there can be some disadvantages to their use. The review considers new technologies that offer opportunity to assess disease with greater objectivity (reliability, precision and accuracy). One of these, 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 the concepts of reliability, precision and accuracy (agreement) in plant pathology and measurement science. The review briefly describes these concepts and how they are gauged in plant disease assessment. Finally various advantages and disadvantages of the different approaches to disease assessment are described, and for each assessment method some future research priorities are identified that would be of value in better understanding disease assessment, and contribute to the aim of improving and fully realizing the potential of image analysis and hyperspectral imagery in plant disease assessment.