## 3 Results and Discussion  
Conducting modeling studies is crucial in evaluating the mechanical characteristics of fragile and delicate fruit. The statistical summary, including the mean, standard deviation, and ranges of the data utilized for the modeling study, is shown in Table 1.  
In the study, three different methods, namely logistic regression, artificial neural networks, and decision trees, were picked to estimate the deformation energy of cherry tomatoes. The working system is shown in Figure 4. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Average** & **Maximum** & **Minimum** & **SD** \\ \hline Mass (g) & 4.82 & 7.36 & 2.48 & 1.34 \\ Length (mm) & 20.90 & 26.23 & 16.63 & 3.43 \\ Width (mm) & 17.80 & 23.27 & 14.72 & 3.40 \\ Thickness (mm) & 18.43 & 24.32 & 15.47 & 3.08 \\ Geo. Diameter (mm) & 18.96 & 24.50 & 15.82 & 3.28 \\ Sphericity (\%) & 90.65 & 95.15 & 86.19 & 2.58 \\ Surface area (mm\({}^{2}\)) & 1163.61 & 1886.56 & 786.94 & 414.48 \\ Rupture force (N) & 23.38 & 27.61 & 20.79 & 1.78 \\ Firmness (N mm\({}^{-1}\)) & 4.97 & 5.87 & 4.08 & 0.64 \\ Poisson ratio & 0.29 & 0.32 & 0.26 & 0.02 \\ Modulus of elasticity (N mm\({}^{-2}\)) & 2.59 & 3.26 & 1.76 & 0.51 \\ Deformation energy (N mm) & 56.34 & 70.45 & 38.36 & 10.92 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Experimentally measured physico-mechanical properties of cherry tomatoes. When the figure is analyzed, the first stage is the creation and preprocessing of the data set. In the second stage, normalization operations are performed. It is the method applied to reduce the data set to a certain value range and thus increase the success rate. In the third stage, the machine learning methods used in the study are seen. At this stage, there are three methods: artificial neural networks, logistic regression, and support vector regression. The fourth stage is the denormalization stage, where the actual values of the normalized values are learned. In the last stage, success and error analyses of machine learning models are performed. Twelve independent variables were used to estimate the deformation energy of cherry tomatoes, the dependent variable of the study. The independent variables of the study were mass, length, thickness, width, geometric diameter, sphericity, surface area, firmness, rupture force, Poisson ratio, and modulus of elasticity. There are 15 data points for each independent variable. The Min-Max method was used for the normalization of machine learning methods. A linear transformation was performed on the original data. In this normalization, the relationships between the original data values are preserved. This method is a scaling technique in which data is rescaled to be between 0 and 1. For each data set, its minimum value is converted to 0, its maximum value to 1, and all other values in that data set to a decimal between 0 and 1. Having a limited range between 0 and 1 in this process suppresses the effect of outliers and has smaller standard deviations [33]. Min-Max normalization formula:  
\[\mathrm{x}\prime=\frac{\mathrm{x_{i}-x_{\mathrm{min}}}}{\mathrm{x_{max}-x_{ \mathrm{min}}}} \tag{13}\]  
Here, x is the normalized result, x is the value to be normalized, the smallest value in the x min data set, and the largest value in the x max data set. In the machine learning model, the data set is divided into two parts, training and testing. Training data is the first data set used to train machine learning algorithms. Models create their rules using this data. The model generalizes by training with the training set and predicts the data it encounters for the first time. Test data is used to evaluate the performance or accuracy of the model. Here, predictions are compared with actual data. It is evaluated by how effective the training is or how accurately the model works. In cases where the results are insufficient, the model's parameters are changed to give better results in the test. This situation continues until the test result reaches the desired level.