Relevance of hyperspectral image feature to catalase activity in eggplant leaves with grey mold disease

Hyperspectral imaging feature of catalase activity in eggplant leaves stressed by grey mold was researched. Hyperspectral imagings of healthy, slight, moderate and heavy infected eggplant leaves were obtained by hyperspectral imaging system across the wavelength region of 400-100nm and diffuse spectral response of objects from hyperspectral imaging was extracted by ENVI software. Then, different preprocessing methods were used to improve the signal noise ratio (SNR) including smoothing, median filter and normalization et al. The models of hyperspectral imaging response and catalase activity were built by the partial least squares regression (PLSR), least squares support vector machines (LS-SVM) and BP neural network (BPNN). The first two latent variables suggested by PLSR model can qualitatively distinguish healthy, slight, moderate and heavy infected eggplant leaves, the coefficient of determination (R2) of BPNN model built by the nine latent variables recommended by PLSR model is 0.8930 and the root mean square error of prediction (RMSEP) is 2.17×103. It demonstrated that catalase activity in eggplant leaves can be effectively detected and disease degree of eggplant leaves stressed by grey mold can also be effectively distinguished by the hyperspectral imaging technique.