True Positive (TP) | False Negative (FN) | |
False Positive (FP) | True Negative (TN) |
Authors | Collection of Datasets (Samples) | Methods that are applied | Model Performance |
---|---|---|---|
Used the system’s camera to snap images of the raisins. | LR, MLP and |
86.44% | |
900 raising grains and 2 classes | KNN, RT and |
100% | |
1400 images of raisins and top 50 features | ANN and |
92.71% | |
Four different types of raisins’ color pictures were analyzed, yielding 36 colors and 8 form attributes | 96.33% | ||
Obtaining raisins’ size and color characteristics using classification techniques | Image Processing Technique | 96% | |
Database for machine learning at UCI different | KNN and |
91.70% | |
Yavuj et al., 2023 | Raisin dataset 2022 from UCI machine Learning repository | 85.44% | |
Yu et al., 2011 | Separated the data into four groups based on appearance, texture, and wrinkling | SVM | 95% |
Images of pumpkin seeds | LR, MLP, |
86.64% |
Start |
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Prediction for raisin grains based on machine learning |
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Parameter tuning |
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Performance Measure Name | Formula |
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Correct Classification Rate |
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Precision |
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Recall |
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F1-score |
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True Positive Rate |
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False Positive Rate |
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Specificity |
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Negative Predictive Value |
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Attribute Name | Attribute Description |
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Area | Determine how many pixels are contained within the raisin and return that value. |
MajorAxisLength | The maximum length of a line that can be drawn on a raisin. |
MinorAxisLength | The minimum length of a line that can be drawn on a raisin. |
Eccentricity | An ellipse, which shares the same moments as a raisin, can be described by its degree of roundness. |
ConvexArea | Provide the size in pixels of the smallest convex shell that contains the raisin region. |
Extent | Provide the fraction of the bounding box’s pixels that are within the raisin region. |
Perimeter | Its circumference can be determined by measuring the distance between the pixels that make up the raisin’s circumference. |
Class | Kecimen and Besni raisin. |
Model Name | Accuracy (%) | Precision (%) | Recall (%) | F1-score (%) | AUC-ROC score (%) |
---|---|---|---|---|---|
Support Vector Machine | 87.78% | 90.37% | 85.92% | 88.00% | 87.88% |
Decision Tree | 86.3% | 91.85% | 82.67% | 87.02% | 86.75% |
Logistic Regression | 87.41% | 88.15% | 86.86% | 87.5% | 87.42% |
Naive Bayes | 84.81% | 90.37% | 81.33% | 85.62% | 85.25% |
K-nearest Neighbours | 87.04% | 90.37% | 84.72% | 87.46% | 87.20% |
Random Forest | 86.30% | 88.15% | 85.00% | 86.55% | 86.35% |
87.41% | 85.51% | 86.45% | 86.31% | ||
XgBoost | 83.70% | 85.19% | 82.73% | 83.95% | 83.73% |
97.41% | 89.19% | 93.10% | 92.57% | ||
Convolution Neural Net. | 81.35% | 83.15% | 79.08% | 81.23% | - |
Radial Basis Function Net. | 83.41% | 84.57% | 81.73% | 83.78% | - |
Recurrence Neural Net. | 73.94% | 74.52% | 72.50% | 73.11% | - |
Artificial Neural Net. | 65.00% | 67.01% | 64.86% | 65.35% | - |
Deep Neural Net. | 69.00% | 70.56% | 67.19% | 68.89% | - |
Model Name | Accuracy (%) | Precision (%) | Recall (%) | F1-score (%) | AUC-ROC score (%) |
---|---|---|---|---|---|
Support Vector Machine | 88.52% | 91.85% | 86.11% | 88.88% | 88.69% |
Decision Tree | 85.93% | 88.89% | 83.92% | 86.33% | 86.05% |
Logistic Regression | 86.67% | 88.89% | 88.37% | 86.95% | 86.74% |
Naive Bayes | 85.93% | 91.11% | 82.55% | 86.61% | 86.32% |
K-nearest Neighbours | 86.30% | 91.11% | 83.11% | 86.92% | 86.64% |
Random Forest | 85.56% | 89.63% | 82.88% | 86.12% | 85.79% |
92.59% | 85.03% | 82.41% | 88.45% | ||
XgBoost | 83.33% | 87.41% | 80.82% | 83.98% | 83.56% |
97.11% | 88.21% | 93.35% | 91.83% | ||
Convilution Neural Net. | 78.51% | 82.22% | 75.54% | 78.91% | - |
Radial Basis Function Net. | 81.11% | 83.31% | 79.02% | 82.37% | - |
Recurrence Neural Net. | 71.29% | 74.10% | 68.53% | 67.40% | - |
Artificial Neural Net. | 64.81% | 65.34% | 62.13% | 63.92% | - |
Deep Neural Net. | 68.27% | 70.59% | 66.19% | 67.78% | - |