PLF-Mamba: Analyzing Individual Milk Yield Dynamics Under Data Scarcity Using Selective State Space Models

Real-world dairy farming datasets are often noisy (e.g., missing or corrupted sensor signals) and contain only short labeled sequences, making conventional correlation analysis and feature prioritization unreliable. We present a robust learning framework that identifies head-specific informative sensor features and predicts daily milk yield by combining reinforcement learning (RL)-based dynamic feature gating with the Mamba architecture. The RL policy samples a binary feature mask to suppress uninformative or corrupted signals to maximize prediction reward, while the Mamba predictor captures long-range dependencies with linear computational complexity. Experiments using the MMCows dataset demonstrate that the proposed framework achieves an average R2 of 0.656 and exhibits substantially lower head-wise variance than Transformer-based baselines, indicating robustness to individual heterogeneity. Ablations removing key components show that RL-based gating is essential: removing the gating module (No-RL) collapses (R2<0). Overall, the proposed approach provides a practical solution for digital livestock farming that mitigates noise and data scarcity while improving robustness across heads.