Monitoring of winter wheat stripe rust by collaborating canopy SIF with wavelet energy coefficients

Abstract

Stripe rust has caused tremendous damage to wheat quality and production. Disease-specific factors revealing group structure and photosynthetic physiology contribute to high-precision monitoring for wheat stripe rust. In this study, we proposed a remote sensing model that collaborates wavelet features (WFs) and solar-induced chlorophyll fluorescence (SIF). First, sensitive features including vegetation indices (VIs), SIF parameters, WFs, and fractional-order derivative spectra (FODs) were screened based on correlation coefficient (CC) analysis and variable importance in projection (VIP). Then, through collaboration among features, six feature sets were received and imported to partial least squares regression (PLSR), back-propagation neural network (BPNN), random forest (RF), and extreme gradient boosting (XGBoost). Finally, monitoring models was evaluated through two methods: holdout cross-validation and 5-fold cross validation to ascertain the optimal feature-algorithm combination. The results demonstrated that the collaboration of canopy SIF with any feature markedly improved the monitoring accuracy due to its responsive nature to the plant's photosynthetic physiology. The model based on XGBoost with WFs-SIF as input features achieved optimal monitoring accuracy, with at least 16.6% increase in R2 and 32.4% reduction in RMSE compared to the VIs-PLSR model. Correlation analysis of evaluation indexes (R2 and RMSE) under two cross-validation methods showed determination coefficients of 0.743 and 0.837, indicating mutual validation and high reliability of the conclusions. This study suggests that the collaboration between WFs and SIF exhibits considerable feasibility in high-precision monitoring of stripe rust, providing a novel insight for future field-scale diagnosis of crop diseases.