P. E. Parker

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

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.

Some Consequences of Using the Horsfall-Barratt Scale for Hypothesis Testing

Comparing treatment effects by hypothesis testing is a common practice in plant pathology. Nearest percent estimates (NPEs) of disease severity were compared with Horsfall-Barratt (H-B) scale data to explore whether there was an effect of assessment method on hypothesis testing. A simulation model based on field-collected data using leaves with disease severity of 0 to 60% was used; the relationship between NPEs and actual severity was linear, a hyperbolic function described the relationship between the standard deviation of the rater mean NPE and actual disease, and a lognormal distribution was assumed to describe the frequency of NPEs of specific actual disease severities by raters. Results of the simulation showed standard deviations of mean NPEs were consistently similar to the original rater standard deviation from the field-collected data; however, the standard deviations of the H-B scale data deviated from that of the original rater standard deviation, particularly at 20 to 50% severity, over which H-B scale grade intervals are widest; thus, it is over this range that differences in hypothesis testing are most likely to occur. To explore this, two normally distributed, hypothetical severity populations were compared using a t test with NPEs and H-B midpoint data. NPE data had a higher probability to reject the null hypothesis (H0) when H0 was false but greater sample size increased the probability to reject H0 for both methods, with the H-B scale data requiring up to a 50% greater sample size to attain the same probability to reject the H0 as NPEs when H0 was false. The increase in sample size resolves the increased sample variance caused by inaccurate individual estimates due to H-B scale midpoint scaling. As expected, various population characteristics influenced the probability to reject H0, including the difference between the two severity distribution means, their variability, and the ability of the raters. Inaccurate raters showed a similar probability to reject H0 when H0 was false using either assessment method but average and accurate raters had a greater probability to reject H0 when H0 was false using NPEs compared with H-B scale data. Accurate raters had, on average, better resolving power for estimating disease compared with that offered by the H-B scale and, therefore, the resulting sample variability was more representative of the population when sample size was limiting. Thus, there are various circumstances under which H-B scale data has a greater risk of failing to reject H0 when H0 is false (a type II error) compared with NPEs.

The Horsfall-Barratt scale and severity estimates of citrus canker.

Citrus canker assessment data were used to investigate effects of using the Horsfall-Barratt (H-B) scale to estimate disease compared to direct estimation to the nearest percent. Twenty-eight raters assessed each of two-hundred infected leaves (0–38% true diseased area). The data were converted to the H-B scale. Correlation (r) showed that direct estimates had higher inter-rater reliability compared to H-B scaled data (r = 0.75 and 0.71 for direct estimates and H-B scaled data, respectively). Lin’s concordance correlation (LCC, ρc) analysis showed individual rater estimates by direct estimation had better agreement with true values compared to H-B scaled data. The direct estimates were more precise compared to H-B scaled data (r = 0.80–0.95 and 0.61–0.90, respectively), but measures of generalised bias or accuracy (Cb) were similar for both methods (0.38–1.00). Cumulative mean disease and cumulative variance of the means were calculated for each rater on a leaf-by-leaf basis. Direct estimates were closer to the true severity 59.5% of the time, and to the cumulative true sample mean 53.7% of the time, and to the cumulative true sample mean variance 63.6% of the time. Estimates of mean severity for each leaf based on estimates by 3, 5, 10, 20 and 28 raters were compared to true disease severity. LCC showed that rater-means based on more raters had better agreement with true values compared to individual estimates, but H-B scale data were less precise, although with means based on ≥ 10 raters, agreement was the same for both assessment methods. Magnitude and dispersion of the variance of the means based on H-B scaled data was greater than that by direct estimates. H-B scaling did not improve reliability, accuracy or precision of the estimate of citrus canker severity compared to direct visual estimation.

Predisposition of citrus foliage to infection with Xanthomonas citri subsp. citri.

Citrus canker;caused by Xanthomonas citri subsp. citri (Xcc);is a serious disease of susceptible citrus in Florida and other citrus-growing areas of the world. The effect of leaf preconditioning as a route for entry of the bacteria is poorly characterized. A series of experiments were designed to investigate the effects of wind and rain (to simulate a storm);high humidity (>90%) and mild abrasion with sand (to simulate wind-blown sand and debris) in predisposing citrus foliage to infection with Xcc. Exposure of leaves of Swingle citrumelo seedlings to wind and rain [16 m sec-1 (58 km hr-1;36 mph) and 235 mm h-1 (9.25 ins h-1);respectively] for 15 or 30 min caused significant injury and disease incidence was twice as high and severity was ten times greater than on leaves on seedlings of unexposed control plants. The points of attachment of the lamina to the petioles were particularly susceptible to wind-induced injury with up to 25% showing symptoms compared to 0% for the unexposed control. Over 80% of injured leaves had lesions associated with the site of injury. There was little or no effect of humidity >90% for 1.5 or 2.5 h on disease incidence or severity compared to the unexposed control. Mild leaf abrasion of grapefruit seedlings with sand increased incidence and severity of disease two-fold. Ways to reduce leaf injury by minimizing wind speed in orchards may contribute to reducing canker incidence and severity.