Prediction of Leaf Break Resistance of Green and Dry Alfalfa Leaves by Machine Learning Methods
Abstract
:1. Introduction
2. Materials and Methods
2.1. Data Set
2.2. Machine Learning Methods
2.3. Extra Trees
2.4. CatBoost
2.5. Gradient Boosting
2.6. Extreme Gradient Boosting
2.7. Random Forest
2.8. Models’ Evaluation Criteria
3. Results and Discussions
3.1. Interpretation of Modeling Results of Dried Alfalfa
3.2. Interpretation of Modeling Results of Green Alfalfa
4. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Nomenclature
ANN | Artificial Neural Network |
CB | CatBoost |
DT | Decision Tree |
ET | Extra Trees |
GB | Gradient Boost |
GPa | Giga Pascal |
H | Hertz |
J | Joule |
MAE | Mean Absolute Error |
MAPE | Mean Absolute Percentage Error |
ML | Machine Learning |
MPa | Mega Pascal |
N | Newton |
RF | Random Forest |
R2 | Correlation Coefficient |
RMSE | Root Mean Square Error |
XGB | Extreme Gradient Boosting |
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Plan Type | Variables | Mean ± SD |
---|---|---|
Dried | Leaf stem diameter (mm) | 0.561 ± 0.157 |
Leaf petiole area (mm2) | 0.261 ± 0.152 | |
Leaf breaking force (N) | 0.031 ± 0.023 | |
Leaf breaking energy (J) | 0.037 ± 0.028 | |
Leaf breaking stress (N mm−2) | 0.158 ± 0.204 | |
Green | Leaf stem diameter (mm) | 0.580 ± 0.157 |
Leaf petiole area (mm2) | 0.280 ± 0.152 | |
Leaf breaking force (N) | 0.087 ± 0.055 | |
Leaf breaking energy (J) | 0.104 ± 0.066 | |
Leaf breaking stress (N mm−2) | 0.412 ± 0.371 |
Plant Type | Model | Evaluation Criteria | |||
---|---|---|---|---|---|
RMSE | MAPE | MAE | R2 | ||
Dried | Extra Trees | 0.0171 | 0.0969 | 0.0099 | 0.9853 |
CatBoost | 0.0174 | 0.1068 | 0.0105 | 0.9838 | |
Gradient Boosting | 0.0265 | 0.1936 | 0.0178 | 0.9624 | |
Random Forest | 0.0306 | 0.2163 | 0.0191 | 0.9499 | |
Extreme Gradient Boosting | 0.0223 | 0.1224 | 0.0124 | 0.9736 | |
Green | Extra Trees | 0.0707 | 0.1604 | 0.0340 | 0.9472 |
CatBoost | 0.0850 | 0.1806 | 0.0387 | 0.9239 | |
Gradient Boosting | 0.1194 | 0.1447 | 0.0621 | 0.8497 | |
Random Forest | 0.0616 | 0.2135 | 0.0363 | 0.9600 | |
Extreme Gradient Boosting | 0.1026 | 0.1750 | 0.0542 | 0.8889 |
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Ercan, U.; Kabas, O.; Moiceanu, G. Prediction of Leaf Break Resistance of Green and Dry Alfalfa Leaves by Machine Learning Methods. Appl. Sci. 2024, 14, 1638. https://doi.org/10.3390/app14041638
Ercan U, Kabas O, Moiceanu G. Prediction of Leaf Break Resistance of Green and Dry Alfalfa Leaves by Machine Learning Methods. Applied Sciences. 2024; 14(4):1638. https://doi.org/10.3390/app14041638
Chicago/Turabian StyleErcan, Uğur, Onder Kabas, and Georgiana Moiceanu. 2024. "Prediction of Leaf Break Resistance of Green and Dry Alfalfa Leaves by Machine Learning Methods" Applied Sciences 14, no. 4: 1638. https://doi.org/10.3390/app14041638
APA StyleErcan, U., Kabas, O., & Moiceanu, G. (2024). Prediction of Leaf Break Resistance of Green and Dry Alfalfa Leaves by Machine Learning Methods. Applied Sciences, 14(4), 1638. https://doi.org/10.3390/app14041638