No abstract

No abstract

Citrus canker is caused by the bacterial pathogen Xanthomonas axonopodis pv. citri and infects several citrus species in wet tropical and subtropical citrus growing regions. Accurate, precise, and reproducible disease assessment is needed for monitoring epidemics and disease response in breeding material. The objective of this study was to assess reproducibility of image analysis (IA) for measuring severity of canker symptoms and to compare this to visual assessments made by three visual raters (VR1-3) for various symptom types (lesion numbers, % area necrotic, and % area necrotic+chlorotic), and to assess inter- and intra-VR reproducibility. Digital images of 210 citrus leaves with a range of symptom severity were assessed on two separate occasions. IA was more precise than VRs for all symptom types (inter-assessment correlation coefficients, r, for lesion numbers by IA = 0.99, by VRs = 0.89 to 0.94; for %, r for % area necrotic+chlorotic for IA = 0.97 and for VRs = 0.86 to 0.89; and r for % area necrotic for IA = 0.96 and for VRs = 0.74 to 0.85). Accuracy based on Lin's concordance coefficient also followed a similar pattern, with IA being most consistently accurate for all symptom types (bias correction factor, Cb = 0.99 to 1.00) compared to visual raters (Cb = 0.85 to 1.00). Lesion number was the most reproducible symptom assessment (Lin's concordance correlation coefficient, ρc, = 0.76 to 0.99), followed by % area necrotic+chlorotic (ρc = 0.85 to 0.97), and finally % area necrotic (ρc = 0.72 to 0.96). Based on the “true” value provided by IA, precision among VRs was reasonable for number of lesions per leaf (r = 0.88 to 0.94), slightly less precision for % area necrotic+chlorotic (r = 0.87 to 0.92), and poorest precision for % area necrotic (r = 0.77 to 0.83). Loss in accuracy was less, but showed a similar trend with counts of lesion numbers (Cb = 0.93 to 0.99) which was more consistently accurately reproduced by VRs than either % area necrotic (Cb = 0.85 to 0.99) or % area necrotic+chlorotic (Cb = 0.91 to 1.00). Thus, visual raters suffered losses in both precision and accuracy, with loss in precision estimating % area necrotic being the greatest. Indeed, only for % area necrotic was there a significant effect of rater (a two-way random effects model ANOVA returned a P < 0.001 and 0.016 for rater in assessments 1 and 2, respectively). VRs showed a marked preference for clustering of % area severity estimates, especially at severity >20% area (e.g., 25, 30, 35, 40, etc.), yet VRs were prepared to estimate disease of <1% area, and at 1% increments up to 20%. There was a linear relationship between actual disease (IA assessments) and VRs. IA appears to provide a highly reproducible way to assess canker-infected leaves for disease, but symptom characters (symptom heterogeneity, coalescence of lesions) could lead to discrepancies in results, and full automation of the system remains to be tested.

Dynamics of dispersal of the bacteria that causes citrus canker (Xanthomonas axonopodis pv. citri) were assessed in simulated wind-driven rain splash. The wind/rain-splash events were simulated using electric blowers to generate turbulent wind (15 to 20 m s-1) and sprayer nozzles to produce water droplets entrained in the wind flow. The splash was blown at an inoculum source of canker-infected trees 1 m downwind. The splash downwind of the source of the infected trees was collected by vertical panel samplers and funnel samplers. The duration over which bacteria were dispersed in spray was assessed in continuous wind at intervals from 0 to 52 h after commencing the simulated rain splash event. In one experiment on 11 February 2003, a total of 1.48 × 106 bacteria were collected by panels 1 m downwind from the inoculum source during the first 10 min of dispersal, but the numbers declined to 3.60 × 105 bacteria after 1 h and ranged between 1.42 × 105 and 1.93 × 104 up to 52 h. In a more detailed study (15 July 2003) of dispersal duration over 4 h, the greatest quantity of bacteria collected by panel samplers were dispersed in the first 5-min period (1.01 × 108 bacteria collected). By 10 min after initiation of dispersal, approximately one-third (3.09 × 107 bacteria collected) of the initial number was being dispersed, and by the end of the first hour, only one-tenth (1.31 × 107 bacteria collected) of the initial quantity was dispersed. Funnel samplers placed at ground level under the trees showed a similar trend. The distance to which bacteria were dispersed in wind-blown splash was also tested under simulated conditions: on 18 September 2003, bacteria were collected by panel samplers at all distances sampled (1, 2, 4, 6, 8, 10, and 12 m) with the greatest number of bacteria deposited at 1 m (4.93 × 106 bacteria collected), while 2.22 × 103 bacteria were deposited over a 10-min period 12 m from the inoculum source. Wind speed declined from 19.5 m s-1 upwind of the trees to 2.8 m s-1 1 m downwind, and by 4 m downwind from the inoculum source, movement was similar to the surrounding air. The data on duration and distance of dispersal were best described by power law regression models compared to exponential models. Citrus canker is readily dispersed in wind-driven rain and is dispersed in large quantities immediately after the stimulus occurs, upon which wind-driven splash can disperse inoculum over a prolonged period and over a substantial distance.

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.