Bock C H, Cook A Z, Parker P E, Gottwald T R
University of Florida/USDA-ARS-USHRL, 2001 S. Rock Rd., Ft. Pierce, FL 34945.
USDA-APHIS-PPQ, Moore Air Base, Edinburg, TX 78539.
Plant Dis. 2009 Jun;93(6):660-665. doi: 10.1094/PDIS-93-6-0660.
Citrus canker (caused by Xanthomonas citri subsp. citri) is a destructive disease, reducing yield and rendering fruit unfit for fresh sale. Accurate assessment of citrus canker severity and other diseases is needed for several purposes, including monitoring epidemics and evaluation of germplasm. We compared measurements of citrus canker severity (percent area infected) from automated image analysis to visual estimates by raters and true values using images from five leaf samples (65, 123, 50, 50, and 200 leaves; disease severity from 0 to 60%). Severity on leaves was measured by automated image analysis by (i) basing threshold values on a presample of leaves, or (ii) replacing healthy leaf color on a leaf-by-leaf basis before automating image analysis. Samples 1 to 4 were assessed by three trained plant pathologists, and sample 5 was assessed by an additional 25 raters. Healthy leaf area color replacement gave the most consistent agreement with the true severity data. Using color replacement, agreement with true values based on Lin's concordance correlation coefficient (ρ) was 0.93, 0.79, 0.71, 0.85, and 0.89 for each of the samples, respectively. The range and consistency of agreement was generally less good for automated thresholds based on a presample (ρ = 0.35-0.90) or visual raters (ρ = 0.30-0.94). The constituents of agreement (precision and accuracy) showed similar trends. No one rater or method was best for every leaf sample, but replacing healthy color in each leaf with a standard color before automation of image analysis improved agreement, and was relatively quick (20 s per image). The accuracy and precision of automated image analysis of citrus canker severity can be comparable to unaided, direct visual estimation by many raters.
柑橘溃疡病（由柑橘溃疡病菌引起）是一种毁灭性病害，会导致产量下降并使果实不适于鲜销。出于多种目的，包括监测疫情和评估种质，需要准确评估柑橘溃疡病的严重程度以及其他病害。我们将自动图像分析得出的柑橘溃疡病严重程度（感染面积百分比）测量结果与评估人员的目视估计值以及使用五个叶片样本（分别为65片、123片、50片、50片和200片叶子；病害严重程度为0%至60%）图像得出的真实值进行了比较。通过以下方式利用自动图像分析测量叶片上的病害严重程度：（i）基于叶片预样本确定阈值，或（ii）在自动图像分析之前逐叶替换健康叶片颜色。样本1至4由三名经过培训的植物病理学家进行评估，样本5由另外25名评估人员进行评估。健康叶片面积颜色替换与真实严重程度数据的一致性最高。使用颜色替换时，基于林氏一致性相关系数（ρ），各样本与真实值的一致性分别为0.93、0.79、0.71、0.85和0.89。对于基于预样本的自动阈值（ρ = 0.35 - 0.90）或目视评估人员（ρ = 0.30 - 0.94），一致性的范围和一致性通常较差。一致性的组成部分（精度和准确性）呈现出类似趋势。对于每个叶片样本，没有一种评估人员或方法是最佳的，但在图像分析自动化之前用标准颜色替换每片叶子中的健康颜色可提高一致性，并且相对较快（每张图像20秒）。柑橘溃疡病严重程度的自动图像分析的准确性和精度可与许多评估人员的直接目视估计相媲美。
