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This paper introduces PLF-Mamba, a framework combining reinforcement learning (RL)-based dynamic feature gating with the Mamba selective state space model to predict daily milk yield from noisy, short-sequence dairy farming datasets. The RL policy learns to mask uninformative sensor features, while Mamba captures long-range dependencies with linear complexity. Experiments on the MMCows dataset demonstrate PLF-Mamba achieves an average R2 of 0.656 and exhibits lower head-wise variance compared to Transformer baselines, highlighting its robustness to individual cow heterogeneity and data scarcity.
Mamba, when combined with RL-based feature gating, can effectively predict milk yield from noisy sensor data, outperforming Transformers in robustness and handling data scarcity.
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.