# YOLOv8m for Automated Pepper Variety Identification: Improving Accuracy with Data Augmentation
## 4 Results  
This study investigated the training of the YOLOv8m convolutional neural network (CNN) to identify eight distinct varieties of peppers. The model's performance was evaluated both with and without the application of data augmentation techniques, considering key metrics such as bounding box precision (BoxP), recall, and mean average precision across different intersection over union (IoU) thresholds (mAP50-95). Table 4 summarizes the evolution of global detection metrics--bounding-box precision (BoxP), recall, mAP\(<\)sub\(>\)50\(<\)/sub\(>\), and mAP\(<\)sub\(>\)50-95\(<\)/sub\(>\)--at four representative training epochs (10, 50, 100, 170) for models trained with and without augmentation. The results indicate that rotation and Gaussian-blur augmentation accelerated convergence and yielded substantial gains. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Epochs** & **BoxP** & \begin{tabular}{c} **BoxP with** \\ **Aug.** \\ \end{tabular} & \begin{tabular}{c} **Recall** \\ **Aug.** \\ \end{tabular} & \begin{tabular}{c} **Recall with** \\ **Aug.** \\ \end{tabular} & \
\begin{tabular}{c} **mAP50 with** \\ **Aug.** \\ \end{tabular} &
\begin{tabular}{c} **mAP50-95** \\ **with Aug.** \\ \end{tabular} \\ \hline
10 & 0.169 & 0.340 & 0.206 & 0.387 & 0.160 & 0.360 & 0.061 & 0.163 \\
50 & 0.526 & 0.705 & 0.570 & 0.605 & 0.562 & 0.696 & 0.278 & 0.369 \\
100 & 0.626 & 0.749 & 0.599 & 0.576 & 0.641 & 0.676 & 0.334 & 0.403 \\
170 & 0.694 & 0.684 & 0.600 & 0.638 & 0.673 & 0.694 & 0.361 & 0.371 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Summary of the model training to identify peppers. Figure 3: Methodology workflow. The results presented in Table 4 indicate the model's performance progression throughout the training process, highlighting the positive effects of data augmentation. The application of these techniques led to improvements across all evaluated metrics. Continuing training beyond 100 epochs revealed the stabilization of performance metrics, indicating that the model had reached an appropriate point of convergence for the dataset. This behavior suggests that increasing the number of epochs beyond this threshold would not yield significant improvements in the model's learning performance. Figure 4 shows that the results display consistent improvements in box precision, recall, and mean average precision (mAP) when augmentation techniques were applied, indicating enhanced generalization capabilities of the model. The comparative analysis between models trained with and without data augmentation techniques revealed consistent improvements in the average values of precision, F1-score, and [EMAIL_ADDRESS]:0.95. These results underscore the importance of incorporating data augmentation methods to enhance the robustness and generalization capacity of object detection models. As emphasized by [PERSON] et al. (2019), applying geometric transformations such as rotation, flipping, and scaling increases the visual diversity of the training dataset. This broader variability enables convolutional neural networks, particularly those based on the YOLO architecture, to detect objects more accurately across different orientations, scales, and visual perspectives. Table 5 provides the class performance values (BoxP, recall, mAP<sub>50</sub>, and mAP<sub>50-95</sub>) for the best non-augmented and augmented models after 170 epochs. The table highlights how varietal morphology affects detector sensitivity and where further targeted data enrichment may be necessary. Figure 4: Comparison of object detection performance metrics between training with and without data augmentation. The data show that the impact of data augmentation varied across classes, with some varieties benefiting more from the technique than others (Supplementary material). Peppers that showed improvement with data augmentation:  
* Cambuci-Pepper achieved the highest gains through data augmentation, reaching 0.852 in BoxP (+14%) and 0.827 in mAP50 (+1.1%).