Detection of Wheat Stripe Rust Using Solar-induced Chlorophyll Fluorescence and Reflectance Spectral Indices

Detection of wheat stripe rust is important for agriculture management and decision，this paper aims to improve detection accuracy of the disease severity of wheat stripe rust by integrating the advantages of reflectance spectroscopy in the detection of crop biochemical parameters and the advantages of chlorophyll fluorescence in photosynthetic physiology diagnosis.Firstly，the solar-induced chlorophyll fluorescence (SIF) at O₂-A band (760 nm) was calculated using the 3FLD algorithm，and seven spectral indices sensitive to wheat stripe rust were investigated for estimating the disease severity.Then，three classic statistical modelling methods，including Support Vector Machine (SVM)，Stepwise Regression (SR) and BP neural network (BP)，were used to quantitatively investigated the performance of the spectral indices and SIF for detection of winter wheat stripe rust severity.The results show that:(1) there is a significantly negative correlation between SIF and the severity of wheat stripe rust.The relationship between SIF and DI can be effectively applied to detect wheat stripe rust.(2) the spectral models based on SIF combined with spectral indices are more accurate than those based on spectral indices.SIF can significantly improve the detection accuracy of the disease severity of winter wheat stripe rust.(3) compared to the SVM and SR methods，the training model constructed by the BP neural network has the highest prediction accuracy whether using the spectral indices or SIF combined spectral indices.However，the verification results show that the disease severity prediction model constructed by SVM and SR method have a better prediction.