## 4 Discussion  
Four significant findings emerge from this investigation. Firstly, and mainly, the capability of estimating the ornamental potential of pepper plants from morphological measurements as well as from a single RGB image has been demonstrated feasible with similar accuracy in both cases. Importantly, this accurate prediction can be achieved both for plants growing under controlled and stress conditions. Second, when employing a single RGB image to predict ornamental potential, the accuracy is kept within acceptable values up to seven weeks in advance. Third, more accurate results are achieved when the image acquisition date is closer to the morphological assessment date, as this reduces uncertainty about the plant's developmental stage. Fourth, increasing the RGB image resolution beyond 200 \(\times\) 300 px does not improve the accuracy and thus seems sufficient for the model to keep enough level of detail about the plant under assessment. Nevertheless, this should be further validated in future work by testing more resolutions with a bigger dataset as well. These demonstrated capabilities show particular promise for agronomic and horticultural use cases, specially in genetic breeding programs, as shown, for example, by a previous review article for ornamental potential prediction by [PERSON] et al. [38]. In particular, being able to accurately predict the ornamental potential of pepper plants is critical for early screening of seedlings and juvenile plants to avoid investing resources in specimens with anticipated poor ornamental characteristics [39]. The models applied in this study demonstrated high accuracy in predicting the ornamental quality of pepper plants, even up to seven weeks in advance. This performance is due to the use of advanced feature transformations combined with deep neural network structures, capable of capturing and qualitatively describing subtle morphological variations between plants [40]. As far as the authors are aware, this work is the first to forecast the ornamental potential of pepper plants (_Capsicum_ spp.) multiple weeks ahead of time using image-based deep learning models. In autogamous plants, such as pepper plants, seven to eight generations are typically required to obtain homozygous lines [41]. Each selection cycle lasts on average 215 days [42], that is, around 31 weeks. In each generation, hundreds or thousands of lines can be evaluated, depending on the crop. In pepper plants, 90 to \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Date** & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline June 22 & 85.02\% & 100\% & 78.36\% & 87.87\% \\ June 29 & 80.57\% & 100\% & 71.93\% & 83.67\% \\ July 6 & 91.90\% & 100\% & 88.30\% & 93.79\% \\ July 13 & 86.64\% & 100\% & 80.70\% & 89.32\% \\ July 20 & 91.90\% & 100\% & 88.30\% & 93.79\% \\ July 27 & 86.64\% & 100\% & 80.70\% & 89.32\% \\ August 3 & 88.26\% & 100\% & 83.04\% & 90.73\% \\ August 10 & 99.20\% & 100\% & 98.83\% & 99.41\% \\ \hline \hline \end{tabular}
\end{table} 
Table 6: ViT-based model performance metrics across dates when assessing the capability for ornamental potential forecasting. All metrics provided in this table have been calculated from Figures 11–88, shown in Appendix A. 730 plants can be evaluated in a single F\({}_{2}\) population [43; 44]. Anticipating each selection cycle in the breeding program by 7 weeks would allow accessions that are unfeasible to be discarded in the 24 th week, which will save time and inputs used to maintain the plants. Thus, this innovative approach not only opens new avenues for integrating phenotyping and forecasting into breeding and production decision-making processes, but also creates opportunities to optimize the management of breeding populations, reduce resource waste, and accelerate the development of new cultivars. The predictive capacity of these models makes them particularly valuable for early-stage phenotypic selection, reducing reliance on late-stage evaluations and subjective visual assessments by experts.