Bock C H, El Jarroudi M, Kouadio L A, Mackels C, Chiang K-S, Delfosse P
United States Department of Agriculture-Agricultural Research Service SEFTNRL, Byron, GA 31008.
Université de Liège, Department of Environmental Sciences and Management, 6700 Arlon, Belgium.
Plant Dis. 2015 Aug;99(8):1104-1112. doi: 10.1094/PDIS-09-14-0925-RE. Epub 2015 Jun 16.
Assessment of disease severity is required for several purposes in plant pathology; most often, the estimates are made visually. It is established that visual estimates can be inaccurate and unreliable. The ramifications of biased or imprecise estimates by raters have not been fully explored using empirical data, partly because of the logistical difficulties involved in different raters assessing the same leaves for which actual disease has been measured in a replicated experiment with multiple treatments. In this study, nearest percent estimates (NPEs) of Septoria leaf blotch (SLB) on leaves of winter wheat from nontreated and fungicide-treated plots were assessed in both 2006 and 2007 by four raters and compared with assumed actual values measured using image analysis. Lin's concordance correlation (LCC, ρ) was used to assess agreement between the two approaches. NPEs were converted to Horsfall-Barratt (HB) midpoints and were compared with actual values. The estimates of SLB severity from fungicide-treated and nontreated plots were analyzed using generalized linear mixed modeling to ascertain effects of rater using both the NPE and HB values. Rater 1 showed good accuracy (ρ = 0.986 to 0.999), while raters 3 and 4 were less accurate (ρ = 0.205 to 0.936). Conversion to the HB scale had little effect on bias but reduced numerically both precision and accuracy for most raters on most assessment dates (precision, r = -0.001 to -0.132; and accuracy, ρ = -0.003 to -0.468). Interrater reliability was also reduced slightly by conversion of estimates to HB midpoint values. Estimates of mean SLB severity were significantly different between image analysis and raters 2, 3, and 4, and there were frequently significant differences among raters (F = 151 to 1,260, P = 0.001 to P < 0.0001). Only on 26 June 2007 did conversion to the HB scale change the means separation ranking of rater estimates. Nonetheless, image analysis and all raters were able to differentiate control and treated-plot treatments (F = 116 to 1,952, P = 0.002 to P < 0.0001, depending on date and rater). Conversion of NPEs to the HB scale tended to reduce F values slightly (2006: NPEs, F = 116 to 276, P = 0.002 to 0.0005; and, for the HB-converted values, F = 101 to 270, P = 0.002 to 0.0005; 2007: NPEs, F = 164 to 1,952, P = 0.001 to P < 0.0001; and, for HB-converted values, F = 126 to 1,633, P = 0.002 to P < 0.0001). The results reaffirm the need for accurate and reliable disease assessment to minimize over- or underestimates compared with actual disease, and the data we present support the view that, where multiple raters are deployed, they should be assigned in a manner to reduce any potential effect of rater differences on the analysis.
在植物病理学中,出于多种目的需要评估病害严重程度;大多数情况下,评估是通过目视进行的。已经确定,目视评估可能不准确且不可靠。评估者有偏差或不精确的评估所产生的影响尚未通过实证数据得到充分探究,部分原因是在不同评估者对同一叶片进行评估时存在后勤困难,而这些叶片在涉及多种处理的重复实验中已测量了实际病害情况。在本研究中,2006年和2007年,由四名评估者对未处理和经过杀菌剂处理地块的冬小麦叶片上的Septoria叶斑病(SLB)进行了最接近百分比估计(NPE),并与使用图像分析测量的假定实际值进行了比较。使用林氏一致性相关性(LCC,ρ)来评估两种方法之间的一致性。NPE被转换为霍斯福尔 - 巴拉特(HB)中点值,并与实际值进行比较。使用广义线性混合模型分析了杀菌剂处理和未处理地块的SLB严重程度估计值,以确定使用NPE和HB值时评估者的影响。评估者1显示出良好的准确性(ρ = 0.986至0.999),而评估者3和4的准确性较低(ρ = 0.205至0.936)。转换为HB量表对偏差影响不大,但在大多数评估日期,对于大多数评估者来说,在数值上降低了精度和准确性(精度,r = -0.001至-0.132;准确性,ρ = -0.003至-0.468)。将估计值转换为HB中点值也略微降低了评估者间的可靠性。图像分析与评估者2、3和4之间的平均SLB严重程度估计值存在显著差异,并且评估者之间也经常存在显著差异(F = 151至1260,P = 0.001至P < 0.0001)。仅在2007年6月26日,转换为HB量表才改变了评估者估计值的均值分离排名。尽管如此,图像分析和所有评估者都能够区分对照和处理地块的处理(F = 116至1952,P = 0.002至P < 0.0001,取决于日期和评估者)。将NPE转换为HB量表往往会略微降低F值(2006年:NPE,F = 116至276,P = 0.002至0.0005;对于转换为HB的值,F = 101至270,P = 0.002至0.0005;2007年:NPE,F = 164至1952,P = 0.001至P < 0.0001;对于转换为HB的值,F = 126至1633,P = 0.002至P < 0.0001)。结果再次强调了需要进行准确可靠的病害评估,以尽量减少与实际病害相比的高估或低估,并且我们提供的数据支持这样一种观点,即在部署多个评估者时,应以减少评估者差异对分析的任何潜在影响的方式进行分配。