Enhancing milled rice qualitative classification with machine learning techniques using morphological features of binary images

Sokudlor, Nuttaphon; Laloon, Kittipong; Junsiri, Chaiyan; Sudajan, Somposh

doi:10.1080/10942912.2023.2264533

Cited by 1 publication

(2 citation statements)

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“…where Q(i) denotes the high-dimensional fluorescence hyperspectral data set x i of the neighborhood data points and m denotes the number of rice fluorescence hyperspectral samples. (2) Keeping Equation (7) unchanged, the low-dimensional space data point A can be obtained by Equation (8).…”

Section: Feature Downscaling and Feature Selectionmentioning

confidence: 99%

“…However, the quality and nutritional value of different rice varieties are different [6], and there is also a significant difference in the selling price. Among them, Thai jasmine rice's appearance, good quality, and fragrant smell are loved by consumers worldwide [7]. However, due to its limited production, the mixing of the expensive Thai jasmine rice with ordinary white rice is becoming an increasingly problematic phenomenon [8].…”

Section: Introductionmentioning

confidence: 99%

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

Kang,

Fan,

Zhan

et al. 2024

Molecules

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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.

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Section: Feature Downscaling and Feature Selectionmentioning

confidence: 99%