Abstract This study aims to assess which spectral variables and at which time late blight can be detected over potato crops. Two experiments were done in a walk-in chamber under controlled environments. To determine the time, the reflectance spectra were plotted as a function of the day post inoculation (DPI), then a Principal Component Analysis (PCA) was applied and the Jeffries–Matusita distance was computed between healthy and infected leaf or canopy spectra. A spectral ratio between infected and healthy cases was used to determine the best wavelengths. Additionally, the spectra were used to compute reflectances and associated vegetation indices for the five bands of the Micasense® RedEdge camera and a Partial Least Square-Discriminant Analysis (PLS-DA) was applied to the reflectance spectra to assess how well the infected leaves or plants can be separated from the healthy ones. The leaf level spectra gives a better separability. A good separability is achieved at 2 and 5 DPI at the leaf and canopy levels, respectively. The best bands are the blue, green, red, and red-edge. The best vegetation indices are SR, Clgreen, RI, TCARI, TCARI/OSAVI-2, ClRed-Edge, and Red-Edge NDVI. The maximum overall accuracy for the PLS-DA is observed at 4 DPI (91.11%) and at 5 DPI (85.93%) at the leaf and canopy level, respectively.

中文翻译：

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使用高光谱数据在叶和冠层级别检测马铃薯晚疫病

摘要 本研究旨在评估可以在马铃薯作物上检测到哪些光谱变量以及在什么时间检测到晚疫病。两个实验是在受控环境下的步入式房间中进行的。为了确定时间，将反射光谱绘制为接种后天数 (DPI) 的函数，然后应用主成分分析 (PCA) 并计算健康和受感染叶片或冠层光谱之间的 Jeffries-Matusita 距离。使用感染病例和健康病例之间的光谱比来确定最佳波长。此外，光谱用于计算 Micasense® RedEdge 相机五个波段的反射率和相关植被指数，并对反射率光谱应用偏最小二乘判别分析 (PLS-DA) 以评估受感染的叶子或植物的能力与健康的人分开。叶级光谱提供了更好的可分离性。分别在 2 DPI 和 5 DPI 时在叶和冠层水平上实现了良好的可分离性。最好的波段是蓝色、绿色、红色和红色边缘。最好的植被指数是 SR、Clgreen、RI、TCARI、TCARI/OSAVI-2、ClRed-Edge 和 Red-Edge NDVI。PLS-DA 的最大整体精度分别在 4 DPI (91.11%) 和 5 DPI (85.93%) 下观察到的叶和冠层水平。分别在 2 DPI 和 5 DPI 时在叶和冠层水平上实现了良好的可分离性。最好的波段是蓝色、绿色、红色和红色边缘。最好的植被指数是 SR、Clgreen、RI、TCARI、TCARI/OSAVI-2、ClRed-Edge 和 Red-Edge NDVI。PLS-DA 的最大整体精度分别在 4 DPI (91.11%) 和 5 DPI (85.93%) 下观察到的叶和冠层水平。分别在 2 DPI 和 5 DPI 时在叶和冠层水平上实现了良好的可分离性。最好的波段是蓝色、绿色、红色和红色边缘。最好的植被指数是 SR、Clgreen、RI、TCARI、TCARI/OSAVI-2、ClRed-Edge 和 Red-Edge NDVI。PLS-DA 的最大整体精度分别在 4 DPI (91.11%) 和 5 DPI (85.93%) 下观察到的叶和冠层水平。