Li Bin, Zhang Dianpeng, Shen Yin
Beijing Research Center of Intelligent Equipment for Agriculture, Beijing, PR China; Key Laboratory of Quantitative Remote Sensing in Agriculture, Ministry of Agriculture, PR China.
Beijing Academy of Forestry and Agriculture Sciences, Beijing, PR China.
Spectrochim Acta A Mol Biomol Spectrosc. 2020 Dec 15;243:118820. doi: 10.1016/j.saa.2020.118820. Epub 2020 Aug 11.
Diseases are critical factors that affect the yield and quality of crops. Therefore, it is of great research value to develop rapid and quantitative methods for identification of common agricultural diseases. This exploratory study involved data analysis of common fungal pathogens using identification modeling based on terahertz spectrum technology. The selected pathogens were Physalospora piricola, Erysiphe cichoracearum, and Botrytis cinerea, which are common fungal pathogens that cause apple ring rot, cucumber powdery mildew, and grape gray mold blight, respectively. Taking polyethylene as the control, the terahertz time-domain spectra, and frequency-domain spectra of samples of the three pathogens were both measured. The absorption and refraction characteristics of these samples in the range of 0.1-2.0 THz were calculated and analyzed, and samples were then divided using the KS algorithm. Terahertz spectrum-image data blocks of the pathogen samples were preprocessed, and the dimensions of data were reduced using non-local mean filtering and the SPA algorithm, respectively. K-nearest neighbors (KNN), support vector machine (SVM), and BP neural network (BPNN), and other algorithms were used for analysis of terahertz images at characteristic frequencies, and for investigating the identification model. The model was quantitatively evaluated, and its imaging visualization was studied. The results suggest that there are significant differences among P. piricola, E. cichoracearum, and B. cinerea in absorption and refraction in the terahertz band. SVM modeling identification results of the three pathogens at the frequency of 1.376 THz were satisfactory, with an R of 0.9649, RMSEP of 0.0273, and a high (93.8212%) comprehensive evaluation index F1-score, and a clearly identifiable visualization effect. This study demonstrated the potential of terahertz spectroscopy to be used for identification of common crop pathogens and has provided technical references for the rapid diagnosis and early warning of agricultural diseases.
病害是影响农作物产量和品质的关键因素。因此，开发快速、定量的常见农业病害鉴定方法具有重要的研究价值。本探索性研究利用基于太赫兹光谱技术的鉴定模型对常见真菌病原体进行了数据分析。所选病原体为苹果轮纹病菌、瓜白粉菌和灰葡萄孢，它们分别是导致苹果轮纹病、黄瓜白粉病和葡萄灰霉病的常见真菌病原体。以聚乙烯为对照，测量了这三种病原体样本的太赫兹时域光谱和频域光谱。计算并分析了这些样本在0.1 - 2.0 THz范围内的吸收和折射特性，然后使用KS算法进行样本划分。对病原体样本的太赫兹光谱图像数据块进行预处理，分别使用非局部均值滤波和SPA算法降低数据维度。使用K近邻（KNN）、支持向量机（SVM）和BP神经网络（BPNN）等算法对特征频率下的太赫兹图像进行分析，并研究鉴定模型。对模型进行了定量评估，并研究了其成像可视化效果。结果表明，苹果轮纹病菌、瓜白粉菌和灰葡萄孢在太赫兹波段的吸收和折射存在显著差异。三种病原体在1.376 THz频率下的SVM建模鉴定结果令人满意，R为0.9649，RMSEP为0.0273，综合评价指标F1分数较高（93.8212%），且可视化效果清晰可辨。本研究证明了太赫兹光谱用于鉴定常见作物病原体的潜力，为农业病害的快速诊断和早期预警提供了技术参考。
