Quality-Gated Episode-Level RGB-D Body Condition Scoring in Beef Cattle

In practical beef cattle monitoring scenarios, automated body condition scoring (BCS) based on single-frame visual information can be affected by posture variation, occlusion, and image quality changes. This study proposed an episode-level BCS assessment framework using synchronized RGB-D videos, integrating multi-frame visual information, attention-based multiple instance learning (Attention-MIL), and a dorsal geometry-based quality control mechanism to improve reliability under complex acquisition conditions. The publicly available Raramuri Criollo dataset was reorganized into a dataset containing 3815 RGB-D frame pairs, 52 animal identity groups, and 151 episodes, and evaluated using animal-wise three-fold cross-validation. Predictions were generated only when an episode contained at least three quality-filtered frames and reliable angle estimation. With an average coverage of 0.7345 after quality gating, the final model achieved 61.66% accuracy, a Macro-F1 score of 0.5680, and a quadratic weighted kappa (QWK) of 0.6339 for five-class BCS classification. Additionally, 89.71% of predictions were within one BCS category of the reference scores. Three-class management classification achieved a Macro-F1 score of 0.8059. Ablation experiments showed complementary contributions from RGB and depth information, and the combined angle and quality-statistic features improved several metrics in the original ablation. Additional matched experiments did not establish an independent angle benefit but supported multi-frame aggregation and within-Episode prediction consistency. The proposed RGB-D multi-frame fusion approach demonstrates potential for automated beef cattle body condition monitoring under controlled acquisition conditions.