# Prediction of Leaf Break Resistance of Green and Dry Alfafla Leaves by Machine Learning Methods
## 3 Results and Discussions
### Interpretation of Modeling Results of Green Alfafla  
Similarly, the machine learning model results established to predict the leaf breaking stress of green alfalfa plants are shown in Table 2. In terms of the R\({}^{2}\) and RMSE metrics, the model built with Random Forest is the most successful. The best R\({}^{2}\) value obtained is 0.96. Accordingly, the independent variables explain approximately 96% of the variance of the dependent variable. The established RF model is successful because the R\({}^{2}\) value is close to 1. The fact that the RMSE value is close to zero indicates that the error obtained from the model is low, which indicates that the established model is successful. The RMSE value is obtained as 0.0616, which is close to 0 and shows the success of the established RF model. In terms of the MAPE metric, the model built with Gradient Boosting is the most successful. The best MAPE value obtained is 0.1447. Accordingly, the leaf breaking stress value of green alfalfa plant can be estimated with an error of approximately 14.5%. This result shows that the model established using the GB method is successful. The worst RMSE, MAPE, and R\({}^{2}\) are 0.1194, 0.2163, and 0.8497, respectively. [PERSON] et al. [70] performed predictions with the value of R\({}^{2}\):0.875 and RMSE: 129.9 with the RF model. In another study, [PERSON] et al. [71] produced predictions with the value of R\({}^{2}\):0.953, MSE: 0.0102 and RMSE: 0.1010 with the Extreme Learning Machine model. [PERSON] et al. [72] produced predictions with the value of R\({}^{2}\):0.9715, MAPE: 0.0146 and RMSE: 15.69 with the CatBoost model, and MAE: 10.63 with the RF model. In this study, while the best RMSE (0.0616) and R\({}^{2}\) (0.96) values for green alfalfa plant were obtained from the model established with the RF method, the best MAE (0.0340) value was obtained from the model established with the ET method. Finally, the best MAPE (0.1447) value was obtained from the model established with the GB method. Figure 6 shows the scatterplots of the machine learning models. While the first graph was produced by the RF model, the second graph was produced by the ET model. Similarly, in the machine learning models established to predict the breaking stress value of the green alfalfa plant, the metric results are generally close to each other. When the predicted values and actual values are close to each other, the values will be on the y = x line. However, as the predicted values deviate from the actual values, the values will not lie on this line. It is clearly seen in the scatterplot that the deviations in the model established with the RF method are greater than in the model established with the ET method. Accordingly, it can be said that the model established with the ET method makes more successful predictions. It is also possible to note that the figures support the metric results shown in Table 2. The models performed better on some metrics, with small differences. Although the model established using the ET method was the most successful model in the MAE metric, all the models obtained similar results. The differences between the models were negligible. Therefore, it can be said that all models achieved successful results in the MAE metric. We can also make this comment for the RMSE metric. In the RMSE metric, the model established with the RF method is more successful with small differences compared to the models established with other methods. The models established with RF, ET, and CB have similar values in the RMSE metric. On the other hand, the model established with the RF method is slightly more successful than the models established with the other methods. A similar situation is valid for the R\({}^{2}\) metric. Although the model established with the RF method is slightly more successful than the models established with other methods, the  
models established with the ET and CB methods also have similar values.