# YOLOv8m for Automated Pepper Variety Identification: Improving Accuracy with Data Augmentation
## 4 Results
* Chilli-Pepper also identified significant improvements, particularly in recall (+22%) and mAP50-95 (+31%). The peppers with more stable or decreased performance with data augmentation were the Fidalga Pepper, which showed a reduction in precision (BoxP dropped from 0.768 to 0.652), suggesting that the model may have been affected by noise in the newly generated samples, and Habanero-Pepper, which also showed a decline in BoxP (from 0.636 to 0.577); however, it showed an improvement in recall (+8.8%), indicating that more objects were detected, even though with lower precision. The peppers that consistently performed well under both conditions were the Jalapeno pepper and Scotch Bonnet pepper, which exhibited minimal variation between tests. This result suggests that the model effectively captures their visual characteristics, regardless of the training strategy. Observations of the results on test images also raise several considerations. Figure 5 presents eight subfigures (a to h) showing the YOLOv8m model's detection of each pepper class on test images. The eight pepper varieties evaluated in the study are (a) Biquinho, (b) Bode, (c) Cambuci, (d) Chilli, (e) Fidalga, (f) Habanero, (g) Jalapeno, and (h) Scotch Bonnet. The bounding boxes inside the images highlight the detections made by the model, along with the confidence scores assigned to each prediction. The confidence values vary from image to image, as they depend on the specific conditions of each sample evaluated by the model. This variation in confidence arises from multiple factors, including lighting and contrast, the position and angle of the peppers, instance overlap, image quality, and resolution. Such fluctuations in confidence values are expected and reflect the model's robustness when dealing with diverse capture conditions. However, the variability in prediction confidence suggests that the model can still be optimized to reduce fluctuations in values across different images. Strategies such as adjusting the detection threshold, refining class balance, and implementing post-processing techniques could help improve the consistency of predictions. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Class** & **BoxP with** & **Recall** & **Recall with** & **mAP50** & **mAP50 with** & **mAP50-95** & **mAP50-95** \\  & **Aug.** & & **Aug.** & **Aug.** & **Aug.** & **Aug.** & **with Aug.** \\ \hline Biquinho-Pepper & 0.693 & 0.658 & 0.516 & 0.486 & 0.636 & 0.562 & 0.692 & 0.386 \\ Bode-Pepper & 0.618 & 0.638 & 0.436 & 0.564 & 0.523 & 0.597 & 0.566 & 0.293 \\ Cambuci-Pepper & 0.747 & 0.852 & 0.758 & 0.777 & 0.816 & 0.827 & 0.884 & 0.596 \\ Chilli-Pepper & 0.713 & 0.750 & 0.595 & 0.729 & 0.648 & 0.790 & 0.622 & 0.345 \\ Fidalga-Pepper & 0.768 & 0.652 & 0.579 & 0.592 & 0.701 & 0.679 & 0.626 & 0.377 \\ Habanero-Pepper & 0.636 & 0.577 & 0.613 & 0.667 & 0.641 & 0.693 & 0.748 & 0.675 \\ Jalapeno-Pepper & 0.622 & 0.595 & 0.775 & 0.75 & 0.702 & 0.721 & 0.422 & 0.489 \\ ScotchBonnet-Pepper & 0.756 & 0.746 & 0.527 & 0.542 & 0.720 & 0.708 & 0.404 & 0.415 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Result of each class in the last training.