<![CDATA[Conventional goat barn management faces critical productivity drawbacks due to unmonitored microclimatic fluctuations, where environmental heat stress and toxic gas accumulations suppress livestock welfare. This study proposes "CapraFarm," a proactive thermal control architecture that integrates ambient temperature, relative humidity, and ammonia gas monitoring into a multi-criteria predictive automation pipeline. To stress-test algorithmic resilience against physical hardware degradation, a 5,000-entry dataset derived from sensor calibration error margins was injected with 5% to 10% Additive Gaussian White Noise. Six machine learning algorithms—including Random Forest, Extra Trees, LightGBM, XGBoost, Support Vector Machines, and Gaussian Naive Bayes—alongside conventional and hysteresis-enhanced rule-based baselines were evaluated using Stratified 10-Fold Cross-Validation. Experimental results revealed that Random Forest achieved the highest predictive performance, maintaining a classification accuracy of 97.12% ± 0.89%, a macro F1-score of 0.9354, and an Area Under Curve of 0.9941. This performance significantly outperformed the hysteresis-enhanced rule-based baseline (94.58% accuracy) under McNemar’s statistical test (). Feature importance attribution using SHapley Additive exPlanations confirmed that ammonia gas spikes exert critical non-linear overrides for instant hazard mitigation. Hardware-in-the-Loop profiling established that while Random Forest is optimal for centralized cloud optimization, Gaussian Naive Bayes excels in edge microcontroller environments with a minimal memory footprint of 45 Kilobytes and an execution latency of 1.94 milliseconds. This study solidifies a dual-layer Hybrid Edge-Cloud Paradigm for continuous climate control, laying a validated computational foundation for future long-term in-vivo physical field deployments in smart livestock facilities.]]>
