Quantitative Assessment of Brix in Grafted Melon Cultivars: A Machine Learning and Regression-Based Approach
Abstract
:1. Introduction
2. Materials and Methods
2.1. Plant Material
2.1.1. Grafting of Melon Seedlings
2.1.2. Plant and Fruit Analysis
2.2. Machine Learning
Support Vector Regression
2.3. Multiple Linear Regression
3. Results and Discussion
4. Conclusions
- Optimizing cultivation practices;
- Improving rootstock selection;
- Enhancing quality control measures;
- Supporting decision-making in commercial melon production.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Diagnostic Tests | To Detect | Results/Interpretation |
---|---|---|
Multicollinearity | VIF | Since each VIF value < 10, there is no multicollinearity problem in the model. |
Heteroscedasticity | Breusch–Pagan/Cook–Weisberg Test | Since p > 0.05, there is no heteroscedasticity in the model. Homoscedasticity refers to the variance of the error term being constant. The residuals are homoscedastic. |
Linearity | Scatter plots and partial regression plots | The predictor variables exhibited a linear relationship with the predicted variable. |
Normality | Shapiro–Wilk W Test | Since p > 0.05, both the residuals and the dependent variable show normal distribution. |
Evaluation Criteria | Equation’s Depiction | Best/Worst Case |
---|---|---|
R2 Coefficient of Determination | 1, 0 | |
MAE Mean Absolute Error | 0, ∞ | |
MAPE Mean Absolute Percentage Error | 0, ∞ | |
MSE Mean Square Error | 0, ∞ | |
RMSE Root Mean Square Error | 0, ∞ |
Variables | Min | Max | SD | Mean | Variables | Min | Max | SD | Mean |
---|---|---|---|---|---|---|---|---|---|
Brix | 5.46 | 6.95 | 0.40 | 6.27 | pH | 5.53 | 5.91 | 0.09 | 5.74 |
Nitrogen | 1.17 | 3.62 | 0.58 | 2.17 | Titratable Acidity | 0.04 | 0.17 | 0.02 | 0.10 |
Phosphorus | 0.13 | 0.46 | 0.06 | 0.26 | Total Dry Matter | 5.68 | 7.42 | 0.43 | 6.69 |
Potassium | 1.74 | 2.76 | 0.25 | 2.20 | Ash | 0.31 | 0.49 | 0.04 | 0.40 |
Calcium | 0.00 | 0.17 | 0.04 | 0.09 | Total Phenolic | 109.90 | 192.80 | 19.36 | 145.91 |
Magnesium | 0.05 | 0.21 | 0.03 | 0.13 | Sucrose | 1.19 | 2.87 | 0.46 | 1.86 |
Zinc | 2.46 | 9.86 | 2.01 | 5.29 | Glucose | 1.54 | 2.07 | 0.12 | 1.83 |
Manganese | 1.39 | 5.45 | 1.05 | 2.50 | Fructose | 1.66 | 2.71 | 0.19 | 2.17 |
Copper | 4.47 | 7.95 | 0.86 | 5.74 | Fruit Weight | 1.29 | 3.19 | 0.39 | 2.28 |
Brix | Coefficient | Std. err. | t | p > |t| |
---|---|---|---|---|
Nitrogen (N) | −0.065 | 0.022 | −2.930 | 0.004 |
Phosphor (P) | −0.690 | 0.223 | −3.100 | 0.002 |
Potassium (K) | 0.112 | 0.047 | 2.410 | 0.017 |
Calcium (Ca) | −2.419 | 0.340 | −7.120 | 0.000 |
Magnesium (Mg) | 1.970 | 0.391 | 5.040 | 0.000 |
Zinc (Zn) | −0.128 | 0.011 | −11.760 | 0.000 |
Manganese (Mn) | 0.207 | 0.021 | 9.950 | 0.000 |
Copper (Cu) | 0.106 | 0.011 | 9.910 | 0.000 |
pH | 0.655 | 0.156 | 4.200 | 0.000 |
Titratable Acidity | 0.455 | 0.386 | 1.180 | 0.240 |
Dry Matter | 0.212 | 0.043 | 4.930 | 0.000 |
Ash | −1.350 | 0.275 | −4.910 | 0.000 |
T. Phenolic | 0.003 | 0.001 | 5.000 | 0.000 |
Sucrose | 0.617 | 0.048 | 12.730 | 0.000 |
Glucose | 1.235 | 0.134 | 9.230 | 0.000 |
Fructose | 0.089 | 0.046 | 1.920 | 0.056 |
Fruit Weight | −0.252 | 0.037 | −6.850 | 0.000 |
Constant | −2.499 | 0.930 | −2.690 | 0.008 |
Rootstocks | n | Mean | Std. Dev. | Min | Max | F | p |
---|---|---|---|---|---|---|---|
Sphinx | 100 | 6.32 | 0.39 | 5.87 | 6.92 | 1.21 | 0.2997 |
Albatros | 100 | 6.23 | 0.41 | 5.46 | 6.95 | ||
Dinero | 100 | 6.26 | 0.38 | 5.63 | 6.84 |
Method | MLR vs. SVR | |||||
---|---|---|---|---|---|---|
Evaluation Criteria | MAE | MAPE | MSE | RMSE | R2 | |
Partition | Training | 0.0675/0.0299 | 0.0108/0.0048 | 0.0076/0.0012 | 0.0871/0.034 | 0.9503/0.9924 |
Testing | 0.0728/0.0334 | 0.0117/0.0054 | 0.0088/0.0016 | 0.0936/0.0398 | 0.9472/0.9904 |
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Share and Cite
Ercan, U.; Sonmez, I.; Kabaş, A.; Kabas, O.; Calık Zyambo, B.; Gölükcü, M.; Paraschiv, G. Quantitative Assessment of Brix in Grafted Melon Cultivars: A Machine Learning and Regression-Based Approach. Foods 2024, 13, 3858. https://doi.org/10.3390/foods13233858
Ercan U, Sonmez I, Kabaş A, Kabas O, Calık Zyambo B, Gölükcü M, Paraschiv G. Quantitative Assessment of Brix in Grafted Melon Cultivars: A Machine Learning and Regression-Based Approach. Foods. 2024; 13(23):3858. https://doi.org/10.3390/foods13233858
Chicago/Turabian StyleErcan, Uğur, Ilker Sonmez, Aylin Kabaş, Onder Kabas, Buşra Calık Zyambo, Muharrem Gölükcü, and Gigel Paraschiv. 2024. "Quantitative Assessment of Brix in Grafted Melon Cultivars: A Machine Learning and Regression-Based Approach" Foods 13, no. 23: 3858. https://doi.org/10.3390/foods13233858
APA StyleErcan, U., Sonmez, I., Kabaş, A., Kabas, O., Calık Zyambo, B., Gölükcü, M., & Paraschiv, G. (2024). Quantitative Assessment of Brix in Grafted Melon Cultivars: A Machine Learning and Regression-Based Approach. Foods, 13(23), 3858. https://doi.org/10.3390/foods13233858