# Tomato Maturity Estimation Using Deep Neural Network
## 4 Results and Discussion
### Discussion  
These results indicate that our method has comparable performance with other deep learning-based maturity classifiers studied in the field of agriculture, although a four-layer convolutional neural network, which has a shallow-level architecture suitable for practical use, was considered in this study. The results of the maturity classification show that the  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Maturity Class** & **Hue Value** & **Estimated Maturity** & _p-Value_ \\ \hline Green & 0.16 \(\pm\) 0.088 (1) & 0.15 \(\pm\) 0.087 & 0.41 \\ Turning & 0.33 \(\pm\) 0.075 & 0.38 \(\pm\) 0.117 & 0.13 \\ Pink & 0.69 \(\pm\) 0.106 & 0.75 \(\pm\) 0.036 & \textless{}0.01 \\ Red & 0.92 \(\pm\) 0.047 & 0.92 \(\pm\) 0.123 & 0.89 \\ \hline \hline \end{tabular} \({}^{\text{(1)}}\) Average \(\pm\) standard deviation.  
\end{table}
Table 2: Analysis of the estimated tomato maturity, by comparing the hue value with the maturity class.  
Figure 8: Inference times of maturity estimations for detected tomatoes by frame.  
performances were 0.97, 0.89, 0.93, and 0.91 for classification accuracy, with a higher range than in the previous study [19; 20]. In addition, our model had a practical processing time of less than 0.02 s. Although there are differences in the complexity of the problem and the GPU used in other studies, this finding can still make a significant contribution to practical systems.  
In this study, tomato maturity was estimated as a continuous value, and the label distribution was considered to reflect the uncertainty within the maturity stage. The results were compared with the hue value, which is highly correlated with tomato growth [20], showing that not only was a linear relationship observed, but there was also no difference in distribution for each maturity stage between estimated maturity and hue value. It is known that our method shows similar results to previous studies and enables the continuous prediction of maturity values. However, the maturity of a tomato is affected by the distribution of various color features, not just a color channel (e.g., the hue value used in this study). The DNN-based tomato maturity estimation approach proposed in this study has the potential to consider the overall color distribution of the object by conducting hierarchical feature extraction. From the comparison results, the predicted means of the two methods were similar, whereas their distributions differed at each maturity stage. This may indicate that the DNN can consider more features than when simply considering the hue value, although it presented errors with respect to previous research. It is expected that our method can be complemented with accurately labeled tomato images and optimized parameter configuration, thus guiding further training of the model to enhance the accuracy of maturity stage classification. Furthermore, a fully mature tomato can be selected based on the confidence provided in the mean and variance losses. However, our approach only conducted maturity estimations for pre-determined objects, which means that the performance depends on how the target area is determined, including the same tomato object. In addition, the maturity can be overestimated or underestimated if the target is occluded by another object. Securing the accuracy of segmentation in the detection stage is required in order to address this issue, which is outside the scope of this study.