Multi-scale monitoring for hazard level classification of brown planthopper damage in rice using hyperspectral technique

The primary aim of this study was to classify the hazard level of brown planthopper (BPH) damage in rice. Three datasets, including spectral reflectance corresponding to the sensitive wavelengths from rice canopy spectral wavelengths, rice stem spectral wavelengths, and fusion information of rice canopy and stem spectral wavelengths were used for BPH hazard level classification by using different algorithms. Datasets and algorithms were optimized by the BPH hazard level classification effects (which was evaluated by indices of accuracy, precision, recall, F₁, and k-value). The optimized algorithm combination was used to build a hazard level classification model for spectral reflectance corresponding to the sensitive wavelength from the rice canopy spectral images. Results showed that: (1) The spectral reflectance corresponding to the sensitive wavelengths of fusion information dataset performed best in BPH hazard level classification, with the highest accuracy (99.08%), precision (99.31%), recall (98.83%), F₁ (0.99), and k-value (0.99). (2) The optimum algorithm combination was Savitzky-Golay (S-G) smoothing, principal component analysis (PCA) for sensitive wavelength selection, and broad-learning system (BLS) for modeling. (3) The spectral reflectance corresponding to the sensitive wavelengths dataset of rice canopy spectral images achieved accuracy (80.63%), precision (80.28%), recall (77.03%), F₁ (0.79), and k-value (0.74) in classifying BPH hazard level by using the optimum algorithm combination.