As one of the most common mulberry pests, Diaphania pyloalis Walker (Lepidoptera: Pyralididae) has occurred and damaged in the main sericulture areas of China. Naked eye observation, the most dominating method identifying the damage of Diaphania pyloalis, is time-wasting and labor consuming. In order to improve the identification and diagnosis efficiency and avoid the massive outbreak of Diaphania pyloalis, near infrared (NIR) hyperspectral imaging technology combined with partial least discriminant analysis (PLS-DA) and Successive projections algorithm (SPA) algorithm was applied to establish a fast and nondestructive detection method of Diaphania pyloalis larva. Hyperspectral images of samples were collected and corresponding spectra data was extracted, then classification models were established.

Results showed that the mean value of the correct rate of calibration and prediction (M_CR) of PLS-DA model with full variables was 76.65%, nevertheless, the absolute difference between the correct rate of calibration and prediction (AB_CR) value was 31.55%. After variable selection calculation, the AB_CR value was reduced to 2.77% based on SPA-PLS-DA model with 9 selected variables, it showed that the robustness of model was improved. In conclusion, hyperspectral imaging coupled with chemometrics method showed a certain potential in the rapid detection of Diaphania pyloalis larvae on mulberry leaves.