## 3 Results and Discussion
These comments can also be made for the training dataset. Figure 4 shows line plots created with data from the ANN and MLR model results. While the plots in the upper part are created with the data obtained from the training results of the models, the plots in the lower part are created with the data obtained from the test results of the models. Figure 4 illustrates how successful the models created using both ANN and MLR techniques are on the whole in the training set. The lines of both real and estimated values overlap in the plots. On the other hand, in the training partition, it is clearly seen in the plots that the ANN method is more successful than the MLR method, and the metric results also support the plots. It can be concluded that the ANN model outperforms the MLR model in the testing partition. As this situation can be clearly seen in the plots at the bottom of Figure 4, the metric results also support these plots. The box plots and scatter plots for the ANN model's training and testing partitions are displayed in Figure 5. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Technique** & **Evaluation Metrics** & **Training Partition** & **Testing Partition** \\ \hline \multirow{4}{*}{MLR} & MAE & 0.2349 & 0.2573 \\  & RMSE & 0.3048 & 0.3343 \\  & R\({}^{2}\) & 0.8441 & 0.7843 \\  & MAPE & 5.5368 & 5.8199 \\ \hline \multirow{4}{*}{ANN} & MAE & 0.0250 & 0.0720 \\  & RMSE & 0.0331 & 0.0964 \\ \cline{1-1}  & R\({}^{2}\) & 0.9982 & 0.9827 \\ \cline{1-1}  & MAPE & 0.5814 & 1.6240 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Metric results of the MLR and ANN. Figure 4: Line plots for training and testing partitions of ANN and MLR models. Figure 5: Box plots and scatter plots for the training and testing partitions of the ANN model. In recent years, many researchers have been using ML techniques to solve problems in many branches of agricultural science. ML methods are more advantageous compared to traditional statistical techniques such as MLR in solving nonlinear complex problems [33; 75; 76; 77]. Although ML methods are very diverse, the ANN method is preferred in most studies, especially to estimate the amount of solid content in fruits and vegetables, while MLR, one of the traditional statistical methods, is preferred as a comparison. In the study of [PERSON] et al., the amount of soluble solids in loquat fruit was estimated by ANN and MLR. In this study, while, in the ANN model (both in the training and test data sets), MAE and RMSE were lower than in the MLR model, R\({}^{2}\) was greater. In the test data set, the R\({}^{2}\) value of the ANN model is 0.84, while it is 0.64 in the MLR model. The MAE value of the ANN model is 0.138, while it is 0.149 in the MLR model. The RMSE value of the ANN model is 0.177, while it is 0.181 in the MLR model. In this study, while better R\({}^{2}\) values were obtained in both ANN and MLR models compared to [PERSON] et al. 's study, MAE and RMSE results were close to each other. In the study of [PERSON] et al., like that previous study, the ANN model outperforms MLR in all metrics. Many studies have reported that ANN model results are better than MLR model results. Accordingly, it can be said that ML models give better results than classical methods [31; 78; 79; 80; 34]. The box plots and scatter plots for the MLR model's training and testing partitions are displayed in Figure 6.  
In this study, the ANN model was more successful in predicting better results than the MLR model. When comparing the ANN model with the MLR model in predicting the amount of brix, MAE, RMSE and MAPE decreased by 89%, while R\({}^{2}\) increased by 18% in the training data set. When the ANN model was matched with the MLR model in predicting the amount of brix, MAE and MAPE decreased by 72%, RMSE decreased by 71%, and R\({}^{2}\) increased by 25% in the testing data set. Together with these details, graphs are used to visually represent and compare the expected and actual values of the ANN and MLR models during the training and testing phases (Figures 5 and 6).