# Prediction of Leaf Break Resistance of Green and Dry Alfafla Leaves by Machine Learning Methods
## 3 Results and Discussions
### Interpretation of Modeling Results of Dried Alfalfa  
According to the results in Table 2, for the dried alfalfa plant, the model established by the Extra Trees method is more successful in all the metrics. The best MAE value was obtained as 0.0099. The MAE value is close to 0, so it can be said that the model built is successful. The best MAPE value was obtained as 0.0969. Accordingly, the leaf breaking stress value of dried alfalfa plants can be estimated with an error of approximately 10%. This result shows that the model established with the Extra Tree method is successful. Similarly, the R\({}^{2}\) value was obtained as 0.9853. Accordingly, the independent variables explain approximately 98.5% of the variance of the dependent variable. Since the R\({}^{2}\) value obtained is close to 1, the established model is successful. The RMSE is a statistical metric that evaluates the error values of regression models built with machine learning methods. It is value close to zero, which indicates that the error obtained from the model is low, and this indicates that the established model is successful. The RMSE value was obtained as 0.0171. This result is close to 0 and shows the success of the established model. The worst RMSE, MAE, MAPE and R\({}^{2}\) values were obtained from the model established with the Random Forest method. The results for these metrics were 0.0306, 0.0191, 0.2163 and 0.9499, respectively. As a result, the most successful model in predicting the leaf breaking stress value of dried alfalfa was obtained with the ET method, while the worst model was obtained with the RF method. There are no studies that have been found to predict the leaf breaking stress of any plant using machine learning methods. In the field literature, it is seen that regression and classification studies are carried out using artificial neural network, logistic regression, support vector machines, extra trees, light gradient boosting, random forest, and decision tree regression methods ([PERSON] et al., 2014; [PERSON] et al., 2015; [PERSON] et al., 2016). In their study, [PERSON] et al. (2014) made predictions with the value of R\({}^{2}\):0.97 with the artificial neural networks model, R\({}^{2}\):0.91 with the logistic regression, and R\({}^{2}\):0.81 with the decision tree regression model. In another study, [PERSON] et al. (2014) predicted the value of R\({}^{2}\):0.92 with the support vector regression model. [PERSON] et al. (2016) made predictions with the value of R\({}^{2}\):0.76 with the Extra Trees model, R\({}^{2}\):0.73 with the Random Forest and R\({}^{2}\):0.68 with the Light Gradient Boosting model. In this study, the best R\({}^{2}\) (0.9853), RMSE (0.0171), MAE (0.0099) and MAPE (0.0969) values for the dry alfalfa plant was obtained from the model established with the ET method. In the machine learning models established to predict the breaking stress value of the dry alfalfa plant, the metric results were generally close to each other. The models were also more successful, with slightly better results in some metrics. Although the model established by the ET method was the most successful model in the MAE metric, all the