白粉病胁迫下小麦叶片光谱响应与智能分类识别

目的 开展基于高光谱技术的白粉病胁迫下田间小麦光谱的响应研究，实现小麦白粉病感染等级的快速确定。方法 采用光纤光谱仪配合积分球和叶片夹采集大田活体小麦叶片可见-近红外光谱；通过光谱数据拟合得到的SF-SPAD (Spectrum fitting SPAD)值来反映叶绿素含量，对叶片感染白粉病进行初步判定；使用PROSPECT模型进行光谱敏感度分析确定敏感波段；结合主成分分析(Principal component analysis, PCA)降维和支持向量机(Support vector machine，SVM)建模，实现对光谱数据的二分类；根据二分类模型判断的病点百分比对小麦病虫害感染程度进行分级。结果 SF-SPAD值随自下而上的叶序的增大而逐渐上升；SF-SPAD值≤0.90的全是病点，≥1.05的全是好点。光谱敏感度分析确定了敏感波段为可见光波段440~500和540~780 nm，降低了数据维度。确定了感染等级(R)与病点百分比(%)的关系为R₁：0~30%、R₂：30%~50%、R₃：50%~70%、R₄：70%~100%。本研究所建模型适用的检测株数最少为20株。结论 结合SF-SPAD值和光谱PCA-SVM二分类建立的监测模型可以准确、快速地判定小麦白粉病感染与否及感染等级，同时可以降低采样数量、减少地面检测工作量、提高检测效率，是一项实用性强、简单、易推广的智能化监测技术。

Spectral response and intelligent classification of wheat leaves under powdery mildew stress

Objective The response of wheat spectrum to powdery mildew stress based on hyperspectral technique was studied in order to determine the infection grade of powdery mildew quickly.Method The visible-near infrared spectra of wheat leaves were collected by fiber optic spectrometer combined with a integrating sphere and a leaf clip. The spectrum fitting SPAD (SF-SPAD) value was used to reflect the chlorophyll content, so as to preliminarily determine the infection of powdery mildew. Spectral sensitivity analysis was performed using PROSPECT model to identify sensitive bands. We combined dimension reduction by principal component analysis (PCA) and support vector machine (SVM) modeling to realize binary classification of spectral data. The infection degree of wheat was graded according to the percentage of disease spots determined by the PCA-SVM binary classification model.Result The SF-SPAD value increased with the increase of leaf order from bottom to top. Spots with SF-SPAD values less than 0.90 were disease spots, while spots with SF-SPAD values above 1.05 were good spots. The spectral sensitivity analysis identified the sensitive bands as 440-500 and 540-780 nm in the visible region, and therefore reduced the data dimension. The relationship between the infection grade (R) and the percentage of disease spots was determined as R₁: 0-30%, R₂: 30%-50%, R₃: 50%-70%, R₄: 70%-100%. The model established in this assay was suitable when the number of tested plants was above 20.Conclusion The monitoring model based on SF-SPAD and spectral PCA-SVM binary classification can accurately and rapidly determine the infection of wheat powdery mildew and the infection grade, reduce the number of samples, reduce the workload of detection on the ground, and improve the detection efficiency. The monitoring model is an intelligent monitoring technology which is practical, simple and easy to popularize.