基于VSURF-CA的小麦条锈病高光谱病情指数估测模型

doi:10.3864/j.issn.0578-1752.2024.03.005

Abstract

Abstract:

【Objective】Stripe rust is a serious threat to the growth and yield of wheat. Accurate monitoring and diagnostic assessment are fundamental prerequisites for effective prevention and control of stripe rust. The objective of this study is to construct a wheat stripe rust estimation model using remote sensing technology, enable the rapid and precise estimation of the disease index (DI), and to provide technical support for precise prevention and control.【Method】The hyperspectral data of wheat at different growth stages (heading period, grain-filling period, and maturity period) were acquired through the ASD spectrometer. Initially, the variable selection using random forests (VSURF) method, combined with correlation analysis (CA), was applied to select characteristic bands from the original spectrum (OR) and the first-order differential spectrum (FD). Subsequently, the random forest (RF) algorithm was utilized to compare modeling results of characteristic bands from different datasets, identifying the feature set with the most effective model. Further, models such as partial least squares regression (PLSR), extreme gradient boosting (XGBoost), and back-propagation neural network (BPNN) were employed to compare the modeling effects of different feature sets within various algorithms. This comprehensive analysis aimed to determine the optimal estimation model for wheat stripe rust DI across the entire growth period. Simultaneously, to validate the effectiveness of the feature set across different growth stages, the feature set was used to rebuild models during each of the three distinct growth periods.【Result】The comparative analysis of model effects revealed that the VSURF-CA-FD feature set (537 nm in the green range and 821, 846 nm in the near-infrared range) demonstrated the most effective estimation within the RF model, achieving an R² value of 0.89 and an RMSE of 12.34. These feature bands also exhibited precision in models constructed with other algorithms, including XGBoost (R²: 0.87, RMSE: 13.15), BPNN (R²: 0.84, RMSE: 15.19), and PLSR (R²: 0.69, RMSE: 20.92). For models constructed during different growth stages, the early growth stage (heading period) exhibited an R² value of 0.54, RMSE of 1.29, and NRMSE of 0.21, meeting the requirements for disease estimation. In the middle growth stage (grain-filling period), the model performed well with an R² of 0.66, RMSE of 12.24, and NRMSE of 0.21. In the late growth stage (maturity period), the model’s effectiveness surpassed that of the previous two stages, with an R² of 0.75, RMSE of 10.77, and NRMSE of 0.15.【Conclusion】Utilizing characteristic bands selected through the VSURF-CA method, an RF model with excellent estimation accuracy for wheat stripe rust DI can be established. The research outcomes will provide valuable insights and methodologies for predicting early and mid-stage stripe rust DI.

Key words: hyperspectral estimation model, wheat stripe rust, disease index (DI), VSURF, feature selection

MEI GuangYuan, LI Rong, MEI Xin, CHEN RiQiang, FAN YiGuang, CHENG JinPeng, FENG ZiHeng, TAO Ting, ZHAO Qian, ZHAO PeiQin, YANG XiaoDong. A VSURF-CA Based Hyperspectral Disease Index Estimation Model of Wheat Stripe Rust[J].Scientia Agricultura Sinica, 2024, 57(3): 484-499.

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数据集 Dataset	预测变量个数 The number of prediction variables	运行时间 Time (min)
VSURF-OR	9	30.5
VSURF-FD	17	15.3
VSURF-CA-OR	7	13.4
VSURF-CA-FD	6	8.1

A VSURF-CA Based Hyperspectral Disease Index Estimation Model of Wheat Stripe Rust

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