An Explainable AI Framework Combining Multi-Task GCN and Reinforcement Learning for Adaptive Feeding and Animal Health Management in Dairy Farming

Interweaving simulation modelling with XAI based frameworks like Multi-Task Graph Convolutional Networks (Free-Range-Learning, where Deep Reinforcement Learning shown here using Genetic Algorithms &SHapley Additive exPlanations (SHAP) to improve decision-making to produce XAI to dairy farm management. Despite the difficulty inherent to such a large dataset of over 10,000 animal-days of multimodal sensor data input, the developed framework attained highly impressive predictive performance including a classification accuracy of 92.3% along with 90.1% precision and 90.5% recall. For the regression tasks MSE of 0.038 and R² of 0.87 were obtained. Furthermore, the DRL component, trained using Proximal Policy Optimization (PPO), was able to make a 25% improvement in feed efficiency and 12.4% decrease in cost of feeding. by/via GAs led to a 9.7 percent increase in sustainability score. SHAP-based interpretability enabled an average user trust score of 4.3/5 and interpretability score of 88.5% accuracy, demonstrating both the transparency and applicability of this framework to the real world. Collectively, these results highlight the framework’s potential for providing high-quality, low-cost, interpretable solutions suitable for adoption in precision dairy farming paradigms.