<![CDATA[Bovine sperm morphology assessment is an important stage in evaluating semen quality for artificial insemination programs. Manual examination under a microscope is time-consuming and highly dependent on the observer's expertise, motivating the development of automated detection systems using deep learning. This study systematically evaluates the influence of YOLOv8 training configurations for detecting bovine sperm head abnormalities. A total of 27 experimental scenarios were designed by combining three optimizers (SGD, Adam, and RMSProp), three learning rates (0.0001, 0.001, and 0.01), and three training epochs (75, 100, and 125). Contrast Limited Adaptive Histogram Equalization (CLAHE) was applied to improve image quality before model training. Performance was evaluated using Precision, Recall, F1-score, and mAP@0.5. The experimental results indicate that the training configuration significantly affects detection performance. The best performance was achieved using the RMSProp optimizer with a learning rate of 0.01 and 75 training epochs, producing a Precision of 89.81%, Recall of 88.38%, F1-score of 89.08%, and mAP@0.5 of 91.76%. These findings demonstrate that systematic evaluation of training configurations provides practical guidance for improving YOLOv8-based bovine sperm abnormality detection without modifying the network architecture]]>
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