## 2 Materials and Methods
### XGBoost-Based Model Ornamental Classification from Tabular Data  
Another important aspect is assessing the feasibility of accurately estimating ornamental quality from tabular data with morphological measurements, and a later comparison with the estimation obtained from images. This classification is deemed as estimation since the ornamental potential is determined from tabular data obtained at the same date as the date when the assessment is made.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Folder** & **PH** & **CD** & **PH/CD** & **GH** & **LD** & **FC\(\geq\)10** & **OP\({}_{\text{VH}}\)** & **OP\({}_{\text{EXP}}\)** \\ \hline CG/B1/T1 & 23.5 & 23.85 & 0.99 & \multicolumn{1}{c}{\(\text{Erect}\)} & \multicolumn{1}{c}{\(\text{Dense}\)} & Yes & Yes & Yes \\ \(\cdots\) & \(\cdots\) & \(\cdots\) & \(\cdots\) & \(\cdots\) & \(\cdots\) & \(\cdots\) & \(\cdots\) & \(\cdots\) \\ UG/B4/T15 & 33.7 & 31.05 & 1.09 & \multicolumn{1}{c}{\(\text{Intermediate}\)} & \multicolumn{1}{c}{\(\text{Dense}\)} & Yes & No & Yes \\ \hline \hline \end{tabular}
\end{table}
Table 2: Layout of the CSV file used for recording the morphological measurements as well as the ornamental classification of pepper plants (_Capsicum_ spp.). Plant height (PH), canopy diameter (CD), plant height to canopy diameter ratio (PH/CD), growth habit (GH), leaf density (LD), fruit count per plant above or equal to ten (FC \(\geq\) 10), and ornamental potential (OP). There are two different OP values: OP\({}_{\text{VH}}\) stands for the value coming from using the Veiling Holambra Cooperative criterion and OP\({}_{\text{EXP}}\) stands for the value coming from using the experts criterion.  
Figure 3: The organized image collection adopted a multi-level directory system. Level 1: Photography session (Week #). Level 2: Growth conditions (CG: controlled greenhouse; UG: uncontrolled). Level 3: Experimental replicates (Block #). Level 4: Treatment identifiers (T#). Within each treatment directory, nineteen rotational images (I#,JPG) documented each plant at 20\({}^{\circ}\) intervals, including both 0\({}^{\circ}\) and 360\({}^{\circ}\) views to ensure full circumferential coverage. Reproduced from [26] (CC BY 4.0).  
In order to answer this research question, an XGBoost model is employed. The rationale behind the use of this algorithm is that it handles structured data efficiently, it is computationally efficient, and it provides insights about feature importance as well. Since the dataset was small, with only 120 instances, 70% of the data was selected for the train dataset, 10% for the validation dataset, and the remaining 20% for the test dataset. In addition, the importance of each feature is calculated. In XGBoost, the feature importance (gain) measures the extent to which each feature contributes to improving the model's predictive performance across all trees in the ensemble. The importance is calculated based on the total reduction in loss (gain) attributed to each feature when it is used in the splits. All models were run using the scikit-learn library [30].  
Table 3 summarizes the most important hyperparameters employed for the XGBoost model.