## 4 Discussion
Similarly, pepper plants often have leaves and fruits that blend in with the surrounding foliage, making it harder for the model to distinguish them from other plants or background elements. The overall accuracy obtained for batch sizes 10, 20, 30, 40, 50, 60, 70, 80, and 90 was 50%, 51%, 54%, 44%, 51%, 58%, 49%, 53%, and 48%, respectively, as shown in Table 10. This trend indicates a steady improvement in accuracy from batch sizes 10 to 30, a dip at batch size 40, and a peak at batch size 60, followed by a fluctuating pattern. Batch size 10 achieved average precision, recall, and F1 scores of 95%, 84%, and 18%, respectively, while batch size 20 scored 84%, 82%, and 34%. At batch size 30, results of 96%, 78%, and 29% were obtained, whereas batch size 40 scored 87%, 79%, and 20%. For batch size 50, the metrics were 82%, 75%, and 8%, while batch size 60 recorded results of 89%, 83%, and 18%. Batch size 70 yielded results of 93%, 77%, and 17%, while batch 80 achieved scores of 98%, 84%, and 23%. Finally, batch size 90 scored 95%, 82%, and 25%. The accuracy of the predictions fluctuated up to batch size 40, then improved consistently from batch sizes 50 to 80, supporting the findings of [26; 27], which affirmed that increased batch size enhances model accuracy. This is also corroborated by [30; 31], which used YOLOv8 for vegetable disease and wheat seed detection, respectively. The marginal increase in accuracy from batch size 10 (50.6%) to batch size 20 (51.3%) suggests a positive trend. However, the average precision drops significantly from 95% to 84%, implying a higher likelihood of false positives at batch size 20 despite only a modest decrease in recall from 84% to 82%. The F1 score improved notably from 18% to 34%, indicating better balance in minimizing false positives and negatives. Batch size 30 showed a slight improvement in accuracy (53.6%) over batch size 20 (51.3%). The precision increased significantly from 84% to 96%, while the recall decreased from 82% to 78%, suggesting more false negatives. The F1 score decreased from 34% to 29%, reflecting this trade-off. At batch size 40, the accuracy dropped to 44.9%, with precision at 87% and recall at 79%. The F1 score further decreased to 20%, indicating a less optimal balance. Batch size 50 saw the accuracy rise to 51.2%, although precision and recall dropped to 82% and 75%, respectively, with the F1 score plummeting to 8%. Batch size 60 marked an accuracy peak at 57.7%, with precision and recall at 89% and 83%, respectively. The F1 score improved to 18%, indicating better balance. However, the accuracy at batch size 70 fell to 49% despite high precision (93%) and lower recall (77%), resulting in an F1 score of 17%. Batch size 80 saw improved accuracy of 52.8%, with exceptional precision (98%) and higher recall (84%), yielding an F1 score of 23%. This batch size achieved a commendable balance, which is crucial for minimizing false positives and negatives. Finally, the accuracy at batch size 90 decreased to 48.4%, with precision, recall, and F1 scores of 95%, 82%, and 25%, respectively, indicating a slight overall improvement in balance despite the lower accuracy. The initial transition from batch sizes 10 to 60 suggests a consistent trend of increasing accuracy, which indicates that the model benefits from larger batches during training up to a certain point. This progression implies that larger batch sizes contribute positively to the model's ability to identify and classify crop types accurately. However, at batch size 70 a sudden dip in accuracy disrupts the upward trend, prompting a re-evaluation of the relationship between batch size and accuracy. This finding challenges the conventional expectation that increasing batch sizes consistently lead to improved model performance [21; 22; 24]. The subsequent batch sizes of 80 and 90 showed a resurgence in accuracy, introducing a sinusoidal-like oscillation to the results. In general, this study demonstrates that increasing batch size does not always lead to better performance.