Robust Multi-Sensor Machine Learning Models for Goat Barn Microclimate Automation

Abstract

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.

GS Cited Analysis