基于递归特征消除的烟草花叶病高光谱精准识别OA

[Objective]Aiming to address the challenge of effectively capturing and reliably discriminating the subtle spectral changes caused by early infection of tobacco mosaic virus(TMV),a precise hyperspectral re-cognition framework based on physiologically sensitive feature selection and interpretable machine learning was developed,to provide technical support for early TMV detection and precise control.[Method]Hyperspectral(400-1 000 nm)reflectance data of healthy and early TMV infected tobacco leaves were collected.After pre-treatment with whiteboard correction and region of interest extraction,fifty-two candidate vegetation indices,in-cluding normalized pigment chlorophyll index(NPCI)and water index(WI),were calculated.Recursive feature elimination(RFE)was used to select the optimal feature subset.Based on the selected subset,four categories of machine-learning models,including k-nearest neighbors(KNN)and support vector machine(SVM),were de-veloped and comparatively evaluated.Hyperparameters were optimized using random search and grid search,and model generalization performance was assessed using an independent test set.Furthermore,shapley additive explanations(SHAP)was introduced to interpret the best-performing model by quantifying the contribution of key features to classification decisions.[Result]A dual-feature subset consisting of NPCI and WI was obtained through RFE screening,demonstrating that pigment and water-related changes are key information for early TMV identification,which effectively mitigates the curse of dimensionality in hyperspectral data.The KNN model built on the dual-feature subset achieved the best performance,with receiver operating characteristic-area under curve(ROC-AUC)of 0.987,five-fold cross-validation accuracy(ACC)of 0.955 and balanced F score(F1-score)of 0.961.On the independent test set,the ROC-AUC remained as high as 0.960,indicating good generalization performance.SHAP analysis further showed that NPCI and WI were the dominant drivers of model decision-making:an increase in their values positively drove samples to be classified as"TMV-infected",and its variation was consistent with TMV-induced chlorophyll degradation and water-stress processes.[Con-clusion]Early and precise identification of TMV can be achieved using only two vegetation indices(NPCI and WI),substantially reducing model complexity.The proposed"RFE-based feature selection+KNN+SHAP"methodological framework not only improves identification performance but also reveals an early spectral re-sponse mechanism dominated by pigment changes and supplemented by water status variations.This provides a reference for the early and rapid identification of TMV,as well as for the development of low-cost,portable TMV monitoring equipment for tobacco.