Abstract
Investigators discuss new findings in artificial intelligence. According to news reporting from Sichuan Agriculture University by NewsRx journalists, research stated, “A rice classification method for the fast and non-destructive differentiation of different varieties is significant in research at present.” Financial supporters for this research include Sichuan Agricultural University. The news journalists obtained a quote from the research from Sichuan Agriculture University: “In this study, fluorescence hyperspectral technology combined with machine learning techniques was used to distinguish five rice varieties by analyzing the fluorescence hyperspectral features of Thai jasmine rice and four rice varieties with a similar appearance to Thai jasmine rice in the wavelength range of 475-1000 nm. The fluorescence hyperspectral data were preprocessed by a first-order derivative (FD) to reduce the background and baseline drift effects of the rice samples. Then, a principal component analysis (PCA) and t-distributed stochastic neighborhood embedding (t-SNE) were used for feature reduction and 3D visualization display. A partial least squares discriminant analysis (PLS-DA), BP neural network (BP), and random forest (RF) were used to build the rice classification models. The RF classification model parameters were optimized using the gray wolf algorithm (GWO).”
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