基于多源特征融合的玉米大螟危害等级监测研究OA

The Asian corn borer causes stem damage in the early growth stage of maize,disrupting the transport of water and nutrients.The application of non-destructive and precise detection techniques is crucial for optimizing pest control strategies and improving maize production efficiency.A multi-source feature fusion-based method for monitoring the corn borer damage level(CBDL)was proposed,integrating vegetation indices,texture features,and color indices of maize at the three-leaf stage to enhance the overall accuracy of early-stage damage assessment.UAV-mounted RGB and multispectral imaging systems were employed to acquire spectral data during the three-leaf stage.The mahalanobis distance classification(MDC)algorithm under supervised classification was used to distinguish maize from soil,followed by binary masking to remove soil background.Fourteen vegetation indices,including the excess green index(ExG)and soil-adjusted vegetation index(SAVI)were extracted;totally 32 texture features were computed from four bands based on the gray-level co-occurrence matrix(GLCM);and eight color parameters were derived.Features were selected by using the Pearson correlation coefficient(PCC),and machine learning prediction models,including random forest(RF),extreme gradient boosting(XGBoost),K-nearest neighbors(KNN),and categorical boosting(CatBoost)were constructed.Results indicated that multi-source feature fusion significantly improved model prediction overall accuracy.Among all models,the KNN model integrating vegetation,texture,and color features achieved the best overall performance,with an overall accuracy,precision,recall,F1-score,and Kappa coefficient of 91.8%,91.9%,91.8%,89.5%,and 87.4%,respectively.The findings demonstrated the effectiveness of multi-source feature fusion in predicting the damage level of corn borer infestations,and it can provide a reliable technical reference for early detection and control of maize pests.