欧亚国际

赤霉病（FHB）病原体在籽粒形成阶段的生长通过破坏小麦穗的光合过程对小麦生产构成致命威胁。实时无损和频繁的代理检测方法对于控制病原体繁殖和有针对性的杀菌剂应用是必要的。因此，本研究通过光谱和叶绿素荧光检测叶绿素相关表型或特征，用于FHB监测。利用从高光谱反射率（HR）、叶绿素荧光成像（CFI）和高通量表型（HTP）中提取的特征，开发了一种方法，用于连续两年的实验中的无症状到有症状疾病检测。使用Boruta特征选择算法选择疾病敏感特征，并进行机器学习序列前向浮动选择（ML-SFFS）以获得最佳特征组合。结果表明，在刺突-病原体相互作用过程中，生化参数HR、CFI和HTP表现出一致的变化。在所选的疾病敏感特征中，倒数反射率（RR=1/700）表现出最高的决定系数（R2）为0.81，均方根误差（RMSE）为11.1。多元k-最近邻模型的总体准确度为R2=0.92，均方根误差为10.21，优于竞争的多元和单变量模型。发现两到三种特征的组合最适合使用ML-SFFS进行无症状疾病检测，平均分类准确率为87.04%，疾病严重程度为20%时，分类准确率逐渐提高到95%。研究表明，叶绿素相关表型与ML-SFFS的融合可能是作物病害检测的良好选择。

The growth of the fusarium head blight (FHB) pathogen at the grain formation stage is a deadly threat to wheat production through disruption of the photosynthetic processes of wheat spikes. Real-time nondestructive and frequent proxy detection approaches are necessary to control pathogen propagation and targeted fungicide application. Therefore, this study examined the ch\lorophyll-related phenotypes or features from spectral and chlorophyll fluorescence for FHB monitoring. A methodology is developed using features extracted from hyperspectral reflectance (HR), chlorophyll fluorescence imaging (CFI), and high-throughput phenotyping (HTP) for asymptomatic to symptomatic disease detection from two consecutive years of experiments. The disease-sensitive features were selected using the Boruta feature-selection algorithm, and subjected to machine learning-sequential floating forward selection (ML-SFFS) for optimum feature combination. The results demonstrated that the biochemical parameters, HR, CFI, and HTP showed consistent alterations during the spike–pathogen interaction. Among the selected disease sensitive features, reciprocal reflectance (RR=1/700) demonstrated the highest coefficient of determination (R2) of 0.81, with root mean square error (RMSE) of 11.1. The multivariate k-nearest neighbor model outperformed the competing multivariate and univariate models with an overall accuracy of R2 = 0.92 and RMSE = 10.21. A combination of two to three kinds of features was found optimum for asymptomatic disease detection using ML-SFFS with an average classification accuracy of 87.04% that gradually improved to 95% for a disease severity level of 20%. The study demonstrated the fusion of chlorophyll-related phenotypes with the ML-SFFS might be a good choice for crop disease detection.

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