The Rapid Non-Destructive Differentiation of Different Varieties of Rice by Fluorescence Hyperspectral Technology Combined with Machine Learning

Abstract

A rice classification method for the fast and non-destructive differentiation of different varieties is significant in research at present. 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). The results show that FD-t-SNE-GWO-RF is the best model for rice classification, with accuracy values of 99.8% and 95.3% for the training and test sets, respectively. The fluorescence hyperspectral technique combined with machine learning is feasible for classifying rice varieties.

Advancing Visible Spectroscopy through Integrated Machine Learning and Image Processing Techniques

“…In conclusion, the presented research offers a methodology for visible spectroscopy through integrating polynomial regression with machine learning algorithms. The results demonstrate the effectiveness of this approach in accurately predicting the dominant The original wavelengths (in nm) for various vapour lamps are as follows: sodium (589), neon (588.2), copper (578.2), mercury (546.074), and helium (587.562) [22][23][24][25][26].…”

“…Kang et al [22] reported that the machine learning-assisted fluorescence hyperspectral technique was beneficial for classifying rice varieties. Blake et al's [23] literature review on machine learning methods for cancer classification using Raman spectroscopy data highlighted the popularity of deep learning models, the need to address methodological challenges, and the need for benchmark datasets.…”

Hyperspectral and Fluorescence Imaging Approaches for Nondestructive Detection of Rice Chlorophyll

“…The utility of fluorescence spectroscopy has also been harnessed for the differentiation of rice cultivars [26]. Yang et al advocated for the application of Laser-Induced Fluorescence (LIF) in conjunction with a multivariate analytical approach, incorporating Principal Component Analysis (PCA) and Support Vector Machine (SVM), to distinguish various paddy rice varieties [27].…”