# Tomato Maturity Estimation Using Deep Neural Network
## 5 Conclusions  
We aimed to continuously estimate tomato maturity from tomato-specific images, for which a DNN model with mean-variance losses was used to learn the maturity features and label distributions. The model structure consists of four CNN layers in order to extract the features, and the weights in the model were updated considering three losses: cross-entropy, mean, and variance. For maturity estimation in the test stage, the estimated value based on the output probability distribution of four maturity classes was calculated as a normalized value between 0 and 1. The results indicate that the F1-score was approximately 0.91 on average, with a range of 0.85-0.97, thus providing comparable performance to that reported in relevant research. The estimated maturity was evaluated by comparing the probability distribution with the hue value (which has a high linear correlation with the accumulated temperature index commonly used to model crop growth) in the HSV color model, and the comparison was conducted according to the maturity stage. The comparisons indicated that there were no significant differences between the estimated maturities and hue values, except in the pink stage, and that the similarity between the two groups was highest in the red stage.  
Our approach shows that DNN-based distribution learning can be utilized to continuously evaluate tomato maturity and has the advantage of allowing for the evaluation of intermediate classes between specific classes, based on the confidence of classes (i.e., the four maturity stages considered in this study). The results were verified through comparison with a color information index related to tomato growth. It is expected that a higher accuracy of data labeling and more precise classification performance are possible, given that this study was conducted under limited test conditions, such as using sparse maturity stages and uncertain labeled samples determined empirically. Such enhancements may contribute to optimal use in automatic harvesting, including functions related to optimal harvest time determination or prediction.  
Despite these contributions, it was difficult to accurately verify the performance of the proposed model, due to the absence of a measurement value for actual maturity and limitations in its utilization regarding passive maturity feature training. These problems should be resolved by providing accurate matching between tomato images and their actual maturities, which will be conducted as future work.  
Conceptualization, D.-H.L.; methodology, T.K. and D.-H.L.; software, T.K. and D.-H.L.; formal analysis, T.K., J.M.Y. and D.-H.L.; investigation, K.-C.K. and T.C.; writing--original draft preparation, T.K. and D.-H.L.; writing--review and editing, K.-C.K., J.M.Y. and T.C.; project administration, T.C. and D.-H.L. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Korea Institute of Planning and Evaluation for Technology in Food, Agriculture and Forestry (IPET) through the Advanced Agricultural Machinery Industrialization Technology Development Program, funded by the Ministry of Agriculture, Food and Rural Affairs (MAFRA) (320029-03) and it was also supported by the Korea Institute of Planning and Evaluation for Technology in Food, Agriculture and Forestry (IPET) through the Advanced Agricultural Machinery Industrialization Technology Development Program, funded by Ministry of Agriculture, Food and Rural Affairs (MAFRA) (321059-02).  
Not applicable.  
The authors declare no conflict of interest.