Anthracnose diseases on strawberry are destructive and may be caused by three Colletotrichum species: C. acutatum J. H. Simmonds, C. fragariae, and C. gloeosporioides (Maas, 1998; Smith, 1998). C. acutatum is the primary causal agent of anthracnose fruit rot and irregular leaf spot. Both C. fragariae and C. gloeosporioides may infect any above-ground part of the plant and incite anthracnose crown rot, anthracnose fruit rot, and anthracnose leaf spot, also called black leaf spot (Howard and Albregts, 1983). Warm temperatures and high humidity allow these pathogens to produce spores rapidly, and these are easily dispersed throughout production fields by rain splash, people, animals, insects, and equipment.
Anthracnose disease control strategies include planting disease-free plants, good sanitation, and the use of cultural and chemical controls; however, overuse of fungicides has resulted in pathogen resistance and failure of some fungicides to control anthracnose epidemics (Forcelini and Peres, 2018; LaMondia, 1995; Smith and Black, 1993a, 1993b). Ideal anthracnose disease control relies on the development and planting of disease-resistant cultivars. Many years are required to develop anthracnose-resistant strawberry germplasm with desirable plant growth habit, fruit taste and flavor, yield, and resistance to insects and other diseases. To identify disease-resistant germplasm, thousands of seedlings must be produced and evaluated for disease response in the field using natural inoculum or in greenhouse trials relying on artificial inoculation. However, inoculation trials are time-consuming, and many plants may be killed by the disease. This presents a problem for breeders because the plants killed in inoculation trials might have possessed other desired horticultural traits that could be used in their breeding program.
Screening strawberry germplasm for disease resistance using detached leaf assays is an alternative to inoculating whole plants that allows plant disease response to be determined without destroying the plant, reduces the time between inoculation and disease assessment, and confines the pathogen to the laboratory, which allows breeders to test for pathogens or races of pathogens from other geographic areas without transferring the pathogen to the field or risking its introduction to the industry. Inoculating detached leaves with a conidial suspension of Colletotrichum species was shown to be an accurate nondestructive method of identifying anthracnose-resistant germplasm by Miller-Butler et al. (2018).
Disease severity refers to the amount of plant tissue that is diseased (necrotic) and may be expressed as the percentage of plant area destroyed by a pathogen. Percentage or numerical disease assessment scales, such as visual rating scales, are often used for quick assessments of disease severity of plant tissue. Percentage scales often are adapted from the Horsfall-Barratt scale (Horsfall and Barratt, 1945), which contains 12 gradations, from 1% to 100%, with the percent disease varying disproportionately. Visual bias can influence accuracy, and percentage scales may be difficult to use when evaluating plants that exhibit noticeably different amounts of infection (James, 1977; Sherwood et al., 1983; Slopek, 1989). When there is little disease, the rater’s visual focus is drawn to the small amount of necrotic or dark tissue compared with healthy or green tissue. When there is only a small amount of healthy tissue in a very diseased sample, the rater’s visual focus is drawn to the small amount of healthy (green) tissue (Sherwood et al., 1983). Small areas of disease or no disease can be seen and a percentage can be determined fairly accurately, but when the lesion area ranges from 10% to 90%, it is much more difficult to give an accurate percentage. Slopek (1989) compared five variations of a 1 to 5 visual rating scale for estimating the percent diseased leaf area of barley plants and found that two of the five visual rating scales worked well for estimating the leaf disease, were as precise as the Horsfall-Barratt scale, and decreased the time required for disease assessment. Other researchers have suggested the use of an equal interval scale over the Horsfall-Barratt scale for assessing disease severity (Nita et al., 2003) or the use of illustrated assessment keys with standard area diagrams of percentage scales that allows standardization in disease assessment methods for a variety of crops (James, 1977).
Disease symptoms on plants or leaves are often evaluated using numbered grade scales, sometimes referred to as arbitrary, nominal, or ordinal scales, which have some degree of subjective interpretation of the disease by the rater. An ordinal scale of 0 to 5 (0 = no disease, 1 = very slight, 2 = slight, 3 = moderate, 4 = severe, 5 = dead plant) is only interpretable in the arrangement of the order and can only provide qualitative data. Many disease assessment keys are ordinal and cannot quantitatively measure a difference between the values but were satisfactory when used by experienced observers for rating plants or plots in an order of increasing symptom severity (Russell, 1978).
When there is more than one visual rater, good agreement and association between raters are desirable. Understanding the interpretation of results from ordinal data can be explained by the concepts of accuracy (agreement) and precision (association). Accuracy is the raters’ ability to rate disease closest to a true value (such as percent disease measurements determined by image analysis) and precision is the repeatability of the scoring; however, accuracy and precision may not coincide. Statistical analysis helps determine whether raters are interpreting disease the same or very close to the same. The Kappa coefficient (k) (Cohen, 1960) is a measure of the difference between the raters’ agreement and the expected agreement. It determines a proportion of agreement (accuracy), correcting for chance agreement and is scaled to vary from –1 to +1. A negative Kappa coefficient indicates less than chance agreement, zero indicates exactly chance agreement, a positive value indicates better than chance agreement, and +1 indicates perfect agreement. The Kappa coefficient has been interpreted as <0 = less than chance agreement, 0.01 to 0.20 = slight agreement, 0.21 to 0.40 = fair agreement, 0.41 to 0.60 = moderate agreement, 0.61 to 0.80 = substantial agreement, and 0.81 to 0.99 = almost perfect agreement (Viera and Garrett, 2005). Agreement and disagreement are not mutually exclusive in an ordinal rating scale. If two raters see the disease on a plant as slight disease and moderate disease, they are not in complete agreement, but they are not in complete disagreement either because both raters have determined that there is disease on the plant. This problem was addressed with the weighted Kappa coefficient by assigning weights to different degrees of disagreement with lesser weight to agreement because categories are further apart (Cohen, 1960, 1968; Fleiss and Cohen, 1973). The degree of linear association (precision) between two sets of data, such as the two visual raters’ disease severity ratings, can be statistically measured with Pearson’s product moment correlation coefficient (rp). A positive Pearson correlation coefficient designates that both sets of data change in the same direction, and a negative Pearson correlation coefficient designates that both sets of data change in opposite directions.
Precise quantitative analysis can be performed on images of diseased plant tissues using computer software. Electronic images can be stored indefinitely, allowing the researcher to process the images as time permits. Digital imaging and analytical software were used by Wang et al. (2008) to develop a miniaturized strawberry leaf disk antifungal bioassay. Their goal was to determine percent disease caused by a Colletotrichum isolate used for inoculation of a leaf disk, as well as the percent phytotoxicity that might have been caused by the antifungal compounds using 15-mm excised strawberry leaf disks. The leaf disks were dipped in antifungal compounds and then inoculated with the same Colletotrichum species being used in this research. The analyzing software transformed the images to show healthy parts of the leaf as green, diseased parts as black, and parts exhibiting phytotoxicity as gray. In a similar study (Abril et al., 2009), photographs of detached strawberry leaves were used for visual assessment of disease severity in a study testing the efficacy of natural product-based fungicides, and the percent diseased leaf area was assessed with an arbitrary scale of 0 = no disease to 3 = most severe disease. When digital image analysis and visual assessment were both used by Kwack et al. (2005) to determine the percentage of diseased area caused by C. orbiculare on cucumber leaves, visual ratings were significantly higher than the image analysis ratings, and the authors reported processing the digital images took longer. When rating severity of foliar citrus canker symptoms, Bock et al. (2009) also reported image analysis was slower compared with visual assessment; however, image analysis gave more precise and accurate results. In contrast, Nutter et al. (1993) reported image analysis as a measure of disease severity was faster compared with visual assessments.
The objective of this study was to compare visual assessments with image analysis of anthracnose disease on inoculated detached strawberry leaves to determine the degree of agreement and association between the two methods. This research expands on preliminary trials to develop a reliable detached leaf assay for anthracnose resistance in strawberry plants (Miller-Butler et al., 2013).
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