## 3 Results and Discussion
The evaluation of neural networks, logistic regression, and decision tree regression is given in Table 2.  
An R\({}^{2}\) of 1 indicates that the independent variables are strong in explaining the dependent variable and provide a linear curve. R\({}^{2}\) was 96.8% for artificial neural networks, 91.1% for logistic regression, and 81.3% for decision tree regression. These results show that the ideal values are met. Mean squared error (MSE) is a statistical metric that evaluates the error values of machine learning methods and shows that values close to zero have fewer errors. An MSE value close to zero indicates that the error is low. It was regarded that the MSE was 0.005 for artificial neural networks, 0.011 for logistic regression, and 0.013 for decision tree regression. The error rate of the models was found to be low and acceptable. The MAE metric gives the magnitude of the error as a quantity. In the study, it was seen that it was 0.051 for artificial neural networks, 0.091 for logistic regression, and 0.097 for decision tree regression. It is accepted that the MAE value is very good in all three models. When evaluated according to error and success values, it is seen that the most successful and least error models are artificial neural networks, logistic regression, and decision tree regression, respectively. Figure 6 shows the scatter plots of the models. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & \
\begin{tabular}{c} **Artificial Neural** \\ **Networks** \\ \end{tabular} & **Logistic Regression** &
\begin{tabular}{c} **Decision Trees** \\ **Regression** \\ \end{tabular} \\ \hline R\({}^{2}\) & 0.968 & 0.911 & 0.813 \\ MAE & 0.051 & 0.091 & 0.097 \\ MSE & 0.005 & 0.011 & 0.013 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Success and error evaluation of the models used in the study. Figure 6: _Cont._  
Figure 6 shows the scatter plot of the machine learning models used to estimate the deformation energy of the cherry tomatoes. In all three models, there is a positive and strong relationship between the actual value and the predicted results of the deformation energy of cherry tomatoes. As the value of one of the variables increases, the other increases and the points cluster near the line. Figure 7 shows the line plots of the models. Figure 6: Scatter plot for graphical analysis of the results of the machine learning models; (**a**) Artificial neural network; (**b**) Logistic regression; (**c**) Decision tree. Figure 7: Cont. Figure 7 indicates the relationship between actual and predicted values. The figures show the relationship is strong for all three models. When the figures are analyzed in detail, it is seen that the model with the strongest relationship is artificial neural networks. Then, logistic regression and decision trees are viewed, respectively. In the literature, it is seen that studies have been carried out using machine learning methods in this field. In most of these studies, the artificial neural network method is used. In this study, logistic regression and decision tree regression methods were also utilized, besides the artificial neural network method. The independent variables used in the model and the parameters of the machine learning models are different. In addition, when the literature is examined, it is obvious that the most successful model is artificial neural networks, with 95% success [11]. In this study, similar to the literature, the most prospering model was artificial neural networks, but the model's success was 96.8%, which is more successful than in the literature.