Automated Detection of Livestock Gastrointestinal Parasite Eggs and Cysts Using YOLOv8-Based Deep Learning

Parasitic infection is one of the common health problems of livestock in Bangladesh. Due to the country’s climate, heavy monsoon rainfall, low biosecurity in farms, and high humidity, along with the presence of suitable vector organisms, gastrointestinal parasitism remains widespread in cattle and other livestock. The standard method of diagnosis is microscopic examination of fecal samples, but this depends on manual observation, which is time-consuming and can lead to error, mainly because many parasite eggs look similar to each other and samples often contain contaminants that can be mistaken for eggs or cysts. In this study, we applied the YOLOv8 deep learning model for automated detection of parasitic eggs and cysts from microscopic images of livestock fecal samples. Images of clinical cases were collected, annotated, and used to train the model in Python, with batch size 16, auto optimizer, learning rate 0.01, momentum 0.937 and weight decay 0.0005. Training was carried out using Google Colab, and the model was evaluated using precision, recall, F1-score, mAP50 and mAP50-95. The model achieved a precision of 56%, recall of 24%, F1-score of 33.6%, mAP50 of 33%, and mAP50-95 of 22%. The relatively low recall and F1-score indicate that the model still has considerable limitation, largely due to insufficient species-specific training data and the presence of image artifacts. Underrepresentation of some parasite species, such as Trichuris spp., in the dataset also caused class imbalance, which affected the model’s ability to detect these species reliably. Despite these limitations, the study indicates that the YOLOv8 architecture has some potential for detection of parasitic eggs and cysts from microscopic images, and that further work with larger and more balanced datasets may improve performance and applicability in veterinary diagnostics.