The invention discloses a hyperspectral and morphological feature fused rice
planthopper stage and sex identification method, and aims to improve the classification accuracy of the variety,
age and sex of rice planthoppers in the initial stage of nymphs.
Spectral data of rice planthoppers are obtained through a
hyperspectral imaging technology, morphological characteristics are extracted by combining a morphological
image processing technology, and the
spectral data and morphological characteristic data are combined by adopting a multi-
modal data fusion technology. And training and classifying the fused data by using a
machine learning
algorithm, and accurately identifying the type,
age and gender of the rice
planthopper. In order to solve the problem of
data imbalance, an SMOTE
algorithm is adopted to carry out sample
oversampling, and class distribution balance is ensured. The severity of
insect pests is evaluated in real time through automatic counting and
population density generation, and support is provided for prevention and control measures. The screening of characteristic wave bands is expected to provide key
technical support for the development of special multispectral cameras. In addition, the model
interpretability is improved by adopting SHAP analysis, and the contribution of the features to the
classification result is quantified.