Mustafa Ghulam, Zheng Hengbiao, Li Wei, Yin Yuming, Wang Yongqing, Zhou Meng, Liu Peng, Bilal Muhammad, Jia Haiyan, Li Guoqiang, Cheng Tao, Tian Yongchao, Cao Weixing, Zhu Yan, Yao Xia
National Engineering and Technology Center for Information Agriculture, Key Laboratory for Crop System Analysis and Decision Making, Ministry of Agriculture, Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing, China.
National Engineering and Technology Center for Information Agriculture, Jiangsu Key Laboratory for Information Agriculture, Ministry of Agriculture, Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing, China.
Front Plant Sci. 2023 Jan 16;13:1102341. doi: 10.3389/fpls.2022.1102341. eCollection 2022.
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 ( ) 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 = 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.
在籽粒形成阶段，小麦赤霉病（FHB）病原体的生长通过破坏小麦穗的光合作用过程，对小麦生产构成致命威胁。为了控制病原体传播和有针对性地施用杀菌剂，实时无损且频繁的代理检测方法是必要的。因此，本研究从光谱和叶绿素荧光方面研究了与叶绿素相关的表型或特征，用于监测小麦赤霉病。利用从高光谱反射率（HR）、叶绿素荧光成像（CFI）和高通量表型分析（HTP）中提取的特征，开发了一种方法，用于连续两年实验中从无症状到有症状的疾病检测。使用Boruta特征选择算法选择疾病敏感特征，并进行机器学习顺序浮动前向选择（ML-SFFS）以获得最佳特征组合。结果表明，在穗-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。在选定的疾病敏感特征中，倒数反射率（RR = 1/700）的决定系数最高，为0.81，均方根误差（RMSE）为11.1。多元k近邻模型优于竞争的多元和单变量模型，总体准确率为 = 0.92，RMSE = 10.21。发现使用ML-SFFS组合两到三种特征对于无症状疾病检测是最佳的，平均分类准确率为87.04%，对于20%的疾病严重程度水平逐渐提高到95%。该研究表明，将与叶绿素相关的表型与ML-SFFS融合可能是作物病害检测的一个不错选择。
