Hyperspectral imaging technology combined with multivariate data analysis to identify heat-damaged rice seeds

Abstract

Seeds in the drying process due to improper control of process and temperature often cause heat damage to affect seed vigor. How to quickly and nondestructively identify seeds that are heat-damaged plays a key role in agricultural production. In this study, the electric heating constant temperature blast drying oven was used to simulate the drying process of rice seeds and heated for different time (Untreated, 1 h/60 °C, 3 h/60 °C, 5 h/60 °C, 7 h/60 °C, 9 h/60 °C, 11 h/60 °C). The hyperspectral images of rice seeds with different degrees of heat damage were obtained by using a hyperspectral imaging system of 866.4–1701.0 nm. Three preprocessing methods (Savitzky-Golay first derivative, standard normal variate, and multivariate scatter correction) were used to preprocess the original spectral data, three feature extraction algorithms (second derivative, successive projections algorithm, and neighborhood component analysis) were used to extract the feature wavelengths, and three classifier models (k-nearest neighbor, support vector machine, and naive Bayes) were used for modeling analysis. After multivariate data analysis, the multivariate scatter correction-neighborhood component analysis-naive Bayes model performed best and was selected as the best model. Finally, the hyperspectral images of the verification set were visualized based on the object-wise method to show the intuitive classification effect. The results show that the hyperspectral imaging technology is an effective tool for quickly identifying and visualizing rice seeds with different degrees of heat damage.