Determination of Moisture in Rice Grains Based on Visible Spectrum Analysis
Abstract
:1. Introduction
2. Materials and Methods
2.1. Preparation of Rice Samples
2.2. Image Acquisition
2.3. Image Preprocessing for Feature Extraction
2.3.1. Global Descriptors
- The color histogram is rotation and scale invariant and is used under the hypothesis that images with similar color model distributions are semantically similar. The color model is a mathematical representation that usually uses three or four different components [38]. Some common color models are Gray, HSV, and RGB.The grayscale color model (GRAY_CH) defines color by using only one component, lightness, which is measured in values ranging from 0 to 255. The RGB color model (RGB_CH) is a color model with three dimensions—red, green, and blue—that are mixed to produce a specific color. The HSV color model (HSV_CH) is a cylindrical color model that remaps the RGB primary colors into three dimensions that are easier for humans to understand.The color moments (HSV_CM) represent the color distribution by three moments: the average is the first-order moment, the variance is the second-order moment, and the skewness is the third-order moment.
- Local binary patterns (LBP) [39] look at points surrounding a central point and test whether the surrounding points are greater than or less than the central point. It is illumination and translation invariant. The image is converted to grayscale, and a histogram is computed with a mask of P = 16 and R = 10.
- Haralick (HRLK) [40] distinguishes between rough and smooth surfaces using the gray level co-occurrence matrix (GLCM), which uses the adjacency concept in images. It looks for pairs of adjacent pixel values that occur in an image and keeps recording it over the entire image.
- A gray level co-occurrence matrix (GLCM) is a histogram of co-occurring gray-scale values at a given offset over an image [41]. It is created in four directions with the distance between pixels as one. Texture features are extracted from the statistics of this matrix according to the correlation of a couple pixels’ gray-level value at different positions.
2.3.2. Local Descriptors
- Scale Invariant Feature Transform (SIFT) [44] is robustly invariant to image rotations, scale, and limited affine variations, but its main drawback is high computational cost.
- Binary Robust Invariant Scalable Keypoints (BRISK) [45] is invariant to scale, rotation, and limited affine changes. It uses an easily configurable circular sampling pattern from which it computes brightness comparisons to form a binary descriptor string.
- Oriented FAST and Rotated BRIEF (ORB) [15] is invariant to scale, rotation, and limited affine changes.
- AKAZE [46] is invariant to scale, rotation, and limited affine changes and has more distinctiveness at varying scales because of nonlinear scale spaces.
3. Results
3.1. Global Descriptors
3.2. Local Descriptors
4. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Global Descriptors | Dataset | Feature Size | RFC | XGBC | RFR | XGBR | |
---|---|---|---|---|---|---|---|
Color histogram 1D | CRAY_CH | G01 | 92 | 0.66 | 0.55 | 0.66 | 0.6 |
Color histogram 3D | HSV_CH | G01 | 1728 | 0.73 | 0.7 | 0.57 | 0.55 |
Color histogram 3D | RGB_CH | G01 | 8000 | 0.75 | 0.6 | 0.65 | 0.58 |
Color moments | HSV_M | G01 | 9 | 0.51 | 0.55 | 0.57 | 0.67 |
LBP | LBP | G01 | 34 | 0.72 | 0.69 | 0.68 | 0.62 |
GLCM | GLCM | G01 | 24 | 0.53 | 0.51 | 0.58 | 0.62 |
Haralick | HRLK | G01 | 13 | 0.53 | 0.55 | 0.6 | 0.49 |
Color histogram 1D | CRAY_CH | G02 | 20 | 0.81 | 0.66 | 0.63 | 0.7 |
Color histogram 3D | HSV_CH | G02 | 8000 | 0.72 | 0.7 | 0.46 | 0.63 |
Color histogram 3D | RGB_CH | G02 | 8000 | 0.8 | 0.72 | 0.65 | 0.65 |
Color moments | HSV_M | G02 | 9 | 0.73 | 0.72 | 0.65 | 0.71 |
LBP | LBP | G02 | 34 | 0.83 | 0.80 | 0.7 | 0.7 |
GLCM | GLCM | G02 | 13 | 0.73 | 0.71 | 0.69 | 0.69 |
GLCM | HRLK | G02 | 24 | 0.73 | 0.64 | 0.69 | 0.67 |
Color histogram 1D | CRAY_CH | G03 | 44 | 0.96 | 0.92 | 0.92 | 0.87 |
Color histogram 3D | HSV_CH | G03 | 8000 | 0.9 | 0.92 | 0.86 | 0.85 |
Color histogram 3D | RGB_CH | G03 | 8000 | 0.87 | 0.9 | 0.9 | 0.86 |
Color moments | HSV_M | G03 | 9 | 0.75 | 0.77 | 0.72 | 0.7 |
LBP | LBP | G03 | 32 | 0.75 | 0.78 | 0.65 | 0.58 |
GLCM | GLCM | G03 | 13 | 0.87 | 0.85 | 0.83 | 0.84 |
Haralick | HRLK | G03 | 24 | 0.85 | 0.81 | 0.81 | 0.79 |
Mean Difference | Lower Estimation | Upper Estimation | p-Adjusted | |
---|---|---|---|---|
RFR-RFC | −0.037281746 | −0.08877360 | 0.01421011 | 0.2423261 |
XGBC-RFC | −0.003769841 | −0.05526170 | 0.04772201 | 0.9975838 |
XGBR-RFC | −0.069207058 | −0.12069891 | −0.01771520 | 0.0033528 |
XGBC-RFR | 0.033511905 | −0.01797995 | 0.08500376 | 0.3344428 |
XGBR-RFR | −0.031925312 | −0.08341717 | 0.01956654 | 0.3782033 |
XGBR-XGBC | −0.065437216 | −0.11692907 | −0.01394536 | 0.0063473 |
Local Descriptors | Feature Size | RFC | XGBC | RFR | XGBR |
---|---|---|---|---|---|
BRISK | 70 | 0.96 | 0.87 | 0.93 | 0.88 |
SIFT | 80 | 0.93 | 0.85 | 0.79 | 0.74 |
AKAZE | 70 | 0.98 | 0.78 | 0.82 | 0.72 |
ORB | 70 | 0.67 | 0.59 | 0.68 | 0.58 |
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Palacios-Cabrera, H.; Jimenes-Vargas, K.; González, M.; Flor-Unda, O.; Almeida, B. Determination of Moisture in Rice Grains Based on Visible Spectrum Analysis. Agronomy 2022, 12, 3021. https://doi.org/10.3390/agronomy12123021
Palacios-Cabrera H, Jimenes-Vargas K, González M, Flor-Unda O, Almeida B. Determination of Moisture in Rice Grains Based on Visible Spectrum Analysis. Agronomy. 2022; 12(12):3021. https://doi.org/10.3390/agronomy12123021
Chicago/Turabian StylePalacios-Cabrera, Héctor, Karina Jimenes-Vargas, Mario González, Omar Flor-Unda, and Belén Almeida. 2022. "Determination of Moisture in Rice Grains Based on Visible Spectrum Analysis" Agronomy 12, no. 12: 3021. https://doi.org/10.3390/agronomy12123021
APA StylePalacios-Cabrera, H., Jimenes-Vargas, K., González, M., Flor-Unda, O., & Almeida, B. (2022). Determination of Moisture in Rice Grains Based on Visible Spectrum Analysis. Agronomy, 12(12), 3021. https://doi.org/10.3390/agronomy12123021