Application of neural networks to discriminate fungal infection levels in rice panicles using hyperspectral reflectance and principal components analysis

Detecting plant health condition is an important step in controlling disease and insect stress in agricultural crops. In this study, we applied neural network and principal components analysis techniques for discriminating and classifying different fungal infection levels in rice (Oryza sativa L.) panicles. Four infection levels in rice panicles were used in the study: no infection condition, light and moderate infection caused by rice glume blight disease, and serious infection caused by rice false smut disease. Hyperspectral reflectance of rice panicles was measured through the wavelength range from 350 to 2500 nm with a portable spectroradiometer in the laboratory. The spectral response characteristics of rice panicles were analyzed, and principal component analysis (PCA) was performed to obtain the principal components (PCs) derived from different spectra processing methods, namely raw, inverse logarithmic, first, and second derivative reflectance. A learning vector quantization (LVQ) neural network classifier was employed to classify healthy, light, moderate, and serious infection levels. Classification accuracy was evaluated using overall accuracy and Kappa coefficient. The overall accuracies of LVQ with PCA derived from the raw, inverse logarithmic, first, and second derivative reflectance spectra for the validation dataset were 91.6%, 86.4%, 95.5%, and 100% respectively, and the corresponding Kappa coefficients were 0.887, 0.818, 0.939 and 1. Our results indicated that it is possible to discriminate different fungal infection levels of rice panicles under laboratory conditions using hyperspectral remote sensing data.