A Dual-Branch Framework for Livestock Disease Screening Using Multi-Repository Visual Datasets and Symptom-Based Intelligence

• We design a seven-stage harmonization pipeline that consolidates four heterogeneous bovine disease repositories
into a single validated six-class dataset, addressing taxonomy standardization, annotation validation, duplicate
removal, class balancing, and cross-split data leakage
verification.
• We train a YOLOv8m-based multi-class bovine disease
detector on 11,804 harmonized images, achieving an
mAP@50 of 0.7136, and analyze its behavior across
confidence thresholds and disease classes.
• We develop a rule-based symptom assessment module,
built on a veterinary-derived disease–symptom pathology
matrix, that generates ranked differential diagnoses from
farmer-reported clinical observations without requiring
any image input, and we characterize its reliability and
failure modes under simulated ambiguous reporting