Cao Xueren, Luo Yong, Zhou Yilin, Fan Jieru, Xu Xiangming, West Jonathan S, Duan Xiayu, Cheng Dengfa
State Key Laboratory for Biology of Plant Disease and Insect Pests, Institute of Plant Protection, Chinese Academy of Agricultural Sciences, Beijing, China; Key Laboratory of Integrated Pest Management on Tropical Crops, Ministry of Agriculture, Environment and Plant Protection Institute, Chinese Academy of Tropical Agricultural Sciences, Haikou, China.
Department of Plant Pathology, China Agricultural University, Beijing, China.
PLoS One. 2015 Mar 27;10(3):e0121462. doi: 10.1371/journal.pone.0121462. eCollection 2015.
To determine the influence of plant density and powdery mildew infection of winter wheat and to predict grain yield, hyperspectral canopy reflectance of winter wheat was measured for two plant densities at Feekes growth stage (GS) 10.5.3, 10.5.4, and 11.1 in the 2009-2010 and 2010-2011 seasons. Reflectance in near infrared (NIR) regions was significantly correlated with disease index at GS 10.5.3, 10.5.4, and 11.1 at two plant densities in both seasons. For the two plant densities, the area of the red edge peak (Σdr680-760 nm), difference vegetation index (DVI), and triangular vegetation index (TVI) were significantly correlated negatively with disease index at three GSs in two seasons. Compared with other parameters Σdr680-760 nm was the most sensitive parameter for detecting powdery mildew. Linear regression models relating mildew severity to Σdr680-760 nm were constructed at three GSs in two seasons for the two plant densities, demonstrating no significant difference in the slope estimates between the two plant densities at three GSs. Σdr680-760 nm was correlated with grain yield at three GSs in two seasons. The accuracies of partial least square regression (PLSR) models were consistently higher than those of models based on Σdr680760 nm for disease index and grain yield. PLSR can, therefore, provide more accurate estimation of disease index of wheat powdery mildew and grain yield using canopy reflectance.
为了确定种植密度和白粉病感染对冬小麦的影响并预测籽粒产量,于2009 - 2010年和2010 - 2011年的生长季,在Feekes生长阶段(GS)10.5.3、10.5.4和11.1时,对两种种植密度的冬小麦冠层进行了高光谱反射率测定。在两个生长季的两种种植密度下,近红外(NIR)区域的反射率在GS 10.5.3、10.5.4和11.1时均与病情指数显著相关。对于两种种植密度,红边峰值面积(Σdr680 - 760 nm)、差值植被指数(DVI)和三角植被指数(TVI)在两个生长季的三个GS时均与病情指数显著负相关。与其他参数相比,Σdr680 - 760 nm是检测白粉病最敏感的参数。针对两个生长季的三个GS以及两种种植密度,构建了病情严重程度与Σdr680 - 760 nm的线性回归模型,结果表明在三个GS时,两种种植密度的斜率估计值无显著差异。在两个生长季的三个GS时,Σdr680 - 760 nm与籽粒产量相关。对于病情指数和籽粒产量,偏最小二乘回归(PLSR)模型的精度始终高于基于Σdr680760 nm的模型。因此,PLSR能够利用冠层反射率更准确地估计小麦白粉病的病情指数和籽粒产量。