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A new standard area diagram set for assessment of severity of soybean rust improves accuracy of estimates and optimizes resource use

机译:设置用于评估大豆生锈严重程度的新标准区域图提高了估算的准确性,并优化资源使用

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Soybean rust (SBR), caused by Phakopsora pachyrhizi, is the most important yield-damaging fungal disease of soybean due to severe reduction in healthy leaf area and acceleration of leaf fall. In experimental research, SBR severity is estimated visually aided/trained by a standard area diagram (SAD) developed and validated during the mid-2000s (Old SAD). In this study, we propose a new SAD set for SBR with six true-colour diagrams following linear increments (c. 15% increments) amended with four additional diagrams at low (<10%) severities, totalling 10 diagrams (0.2%, 1%, 3%, 5%, 10%, 25%, 40%, 55%, 70%, and 84%). For evaluation, 37 raters were split into two groups. Each assessed severity in a 50-image sample (0.25%-84%), first unaided and then using either the Old SAD or the New SAD. Accuracy, precision, and reliability of estimates improved significantly relative to unaided estimates only when aided by the New SAD (accuracy >0.95). Low precision (<0.78) and a trend of underestimation with an increase in severity were the main issues with the Old SAD, which did not differ from unaided estimates. Simulation to evaluate the impact of the errors by different methods on hypothesis tests, showed that the new SAD was more powerful for detecting the smallest difference in mean control (e.g., 70% vs. 65% disease reduction) than the Old SAD; the latter required a 2-fold increase in sample size to achieve the same power. There is a need to improve some SADs, taking advantage of new knowledge and technology to increase accuracy of the estimates, and to optimize both resource use efficiency and management decisions.
机译:由Phakopsora pachyrhizi引起的大豆生锈(SBR)是由于严重减少健康叶面积和叶坠落加速度,是大豆的最重要产量损伤真菌疾病。在实验研究中,SBR严重程度估计在2000年代中期开发和验证的标准区域图(SAD)视觉上辅助/训练/训练。在这项研究中,我们为SBR提出了一种新的SAD SET,随后用六个真彩图(C.15%增量)修正了4个额外图的额外的速度,总计10个图(0.2%,1 %,3%,5%,10%,25%,40%,55%,70%和84%)。对于评估,将37名评估者分成两组。每次评估50图像样本中的严重程度(0.25%-84%),首先是唯一的,然后使用旧的悲伤或新悲伤。估计的准确性,精度和可靠性,只有在新的悲伤(精确> 0.95)的辅助时,才能有效地改善了无可透明的估计。低精度(<0.78)和低估的趋势随着严重程度的增加是旧悲伤的主要问题,这与无可透明的估计没有不同。仿真以评估不同方法对假设试验的影响,表明,新的悲伤更强大,用于检测平均控制的最小差异(例如,70%与65%疾病减少)而不是旧的悲伤;后者需要增加样品大小的2倍以达到相同的功率。有必要改善一些悲伤,利用新的知识和技术来提高估计的准确性,并优化资源利用效率和管理决策。

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