# YOLOv8m for Automated Pepper Variety Identification: Improving Accuracy with Data Augmentation
## 3 Materials and Methods
### Dataset Partitioning and Preprocessing Strategies  
In the third step, the dataset was then divided into three distinct subsets to increase the volume and diversity of training samples, considering the limitations of the initial dataset:  
* Training Set: 1245 images (84% of the total)
* Validation Set: 201 images (14% of the total)
* Test Set: 30 images (2% of the total)  
Although the final test set comprised only 30 images (1% of the total dataset), this decision prioritized maximizing the volume and diversity of training data, which is critical for enhancing deep learning performance in object detection tasks. Although the test set was relatively small (30 images), it was carefully constructed to ensure visual diversity, encompassing variations in lighting, angles, and fruit morphology, thereby reflecting real-world application scenarios. To reduce the risk of overfitting, data augmentation techniques such as rotation and Gaussian blur were employed to introduce controlled variability and improve the model's generalization capacity. The stabilization of performance metrics after 100 epochs, along with consistent improvements observed with the use of augmented data, indicates a well-adjusted model. Approximately 900 instances were generated for each pepper variety at the end of the process. Subsequently, a second round of model training was conducted using an augmented dataset.  
This methodological approach is consistent with the findings of [PERSON] et al. [35], who verified that in data-scarce scenarios, a larger training partition combined with data augmentation effectively mitigates overfitting, provided the validation and test sets retain  
\begin{table}
\begin{tabular}{c c c} \hline
**Image of the Peppers** & **Name/Variety** & **Description** \\ \hline \multirow{4}{*}{Biouinho/_Capsicum chinense_} & Small, teardrop-shaped, glossy red fruits measuring 2–3 cm long and 1–2 cm in diameter. \\ \cline{2-3}  & & Round and bulbous, yellowish, typically yellow or red, 2–4 cm in both height and diameter, with a distinctly wrinkled surface. \\ \hline \multirow{4}{*}{Cambuci/_Capsicum bacatum_} & Bell-shaped with pronounced lobes, green in color, measuring 4–6 cm in diameter and 3–5 cm in height. \\ \cline{2-3}  & Chilli/_Capsicum annuum_ & Elongated and slender, bright red or chocolate, typically 8–12 cm long and 1–2 cm in diameter. \\ \hline \multirow{4}{*}{Fidalga/_Capsicum chinense_} & Small and round, shiny yellow, about 2–3 cm in diameter and 2–4 cm in height. \\ \cline{2-3}  & Habanero/_Capsicum chinense_ & Oval and lantern-shaped, orange, 3–6 cm long and 2–4 cm in diameter, with a wrinkled texture. \\ \hline \multirow{4}{*}{Jalapeno/_Capsicum annuum_} & Medium-sized, cylindrical, dark green, measuring 5–9 cm in length and 2–4 cm in diameter, with a tapered end. \\ \cline{2-3}  & Scotch Bonnet/_Capsicum chinense_ & Squashed-round shape, yellow, 4–6 cm in both height and diameter, with a wrinkled appearance. \\ \hline \end{tabular}
\end{table}
Table 1: Image, name, variety, and morphological description of the eighth studied pepper.  
representative variability. While this principle guides our methodology, we acknowledge that the limited size of the current test set constrains a definitive evaluation of the risk of overfitting. Future research should incorporate strategies such as cross-validation and an expanded test dataset to enhance the robustness and reliability of the findings.