Powdery mildew disease threatens wheat production worldwide, and early detection is of great significance for disease control and maximizing yield and quality. To improve early remote sensing detection of wheat powdery mildew, solar-induced chlorophyll fluorescence (SIF) parameters were extracted using three-band Fraunhofer line discrimination (3FLD) and reflectance index approaches, and vegetation index (VI) was calculated by hyperspectral reflectance. All features and feature subsets of different data sources were used as inputs to multiple linear regression (MLR), random forest (RF), and support vector machine (SVM) algorithms to construct a wheat powdery mildew monitoring model. SVM includes linear kernel function (LK), polynomial kernel function (PK), and Gaussian radial basis function (RBF). Under wheat powdery mildew stress, wheat canopy reflectance showed a blue shift, and fluorescence weakened. The correlation between SIF−A intensity and disease index (DI) in the O2−A band extracted using the 3FLD method was the highest at −0.781, showing that the SIF parameter was useful for monitoring powdery mildew. Whether based on all features or feature subsets, the RBF model achieved the highest model accuracy, followed by the RF and the MLR. In the feature subset, the accuracy ranges of RBF, LK, and PK models are 0.740−0.871, 0.724−0.850, and 0.716−0.841 respectively. The SIF+VI in the RBF model is more useful for early and stable disease monitoring of wheat powdery mildew. This innovative technical solution is expected to support the early diagnosis of wheat powdery mildew, significantly improving disease prevention and control efficiency and effectiveness.

中文翻译：

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利用太阳诱导的叶绿素荧光和高光谱反射法早期诊断小麦白粉病


白粉病威胁着全世界的小麦生产，早期发现对于病害控制和最大限度地提高产量和质量具有重要意义。为提高小麦白粉病的早期遥感检测能力，采用三波段弗劳恩霍夫线鉴别 （3FLD） 和反射指数方法提取太阳诱导叶绿素荧光 （SIF） 参数，并通过高光谱反射率计算植被指数 （VI）。将不同数据源的所有特征和特征子集作为多元线性回归 （MLR） 、随机森林 （RF） 和支持向量机 （SVM） 算法的输入，构建小麦白粉病监测模型。SVM 包括线性核函数 （LK）、多项式核函数 （PK） 和高斯径向基函数 （RBF）。在小麦白粉病胁迫下，小麦冠层反射率呈蓝移，荧光减弱。使用 3FLD 方法提取的 O2-A 波段中 SIF-A 强度与疾病指数 （DI） 之间的相关性最高，为 -0.781，表明 SIF 参数可用于监测白粉病。无论是基于所有特征还是特征子集，RBF 模型都实现了最高的模型精度，其次是 RF 和 MLR。在特征子集中，RBF 、 LK 和 PK 模型的准确率范围分别为 0.740−0.871、0.724−0.850 和 0.716−0.841。RBF 模型中的 SIF+VI 对小麦白粉病的早期稳定病害监测更有用。这一创新的技术方案有望支持小麦白粉病的早期诊断，显著提高病害防控效率和效果。