| 論文英文摘要 |
Plant disease quantification is crucial for crop disease management, breeding for disease resistance, and the development of disease forecasting systems, as well as for related research and applications. Common approaches to disease quantification include evaluating disease prevalence, incidence, and severity. Traditionally, disease severity has been defined as the percentage of symptomatic area on plant tissues or organs, with the nearest percent estimate (NPE) often used as a measure. However, not all raters can quickly, precisely, and accurately assess NPE. Thus, many studies have proposed the use of quantitative ordinal scales for estimating disease severity. One example is the Horsfall-Barratt scale, which has been widely applied in studies of various plant diseases. Nonetheless, due to its nonlinear scale design, this type of scale has been criticized for introducing bias that may reduce statistical power in subsequent analyses. This article reviews past studies, discusses how to optimize the structure of quantitative ordinal scales to improve the precision and accuracy of disease severity assessments, and introduces recent advances in statistical methods for analyzing quantitative ordinal scale data, aiming to enhance researchers' understanding of the collection and analysis of such data in plant protection research. |