Livestock Monitoring Based on Microclimate and Behavior Tracking Sensors: A Systematic Review

Sensor-based cattle monitoring is a growing field within precision livestock farming, where machine learning algorithms are used to detect health issues, monitor behavior, and improve productivity. However, the diversity of evaluation strategies, performance metrics, and algorithmic choices in the current literature complicates comparison and selection of machine learning models. In this study, we conduct a systematic review and quantitative analysis of machine learning approaches applied to sensor-driven cattle monitoring tasks. We evaluate and compare algorithms using a broad spectrum of performance indicators, including accuracy, F1-score, precision, and recall, alongside their variability across studies. Through detailed heatmap visualizations and statistical summaries (mean, median, standard deviation, coefficient of variation), we identify the most consistently high-performing models and the factors contributing to variability. Results show that Convolutional Neural Networks and Extreme Gradient Boosting achieve the highest F1-scores and most balanced precision-recall performance, especially in imbalanced health detection tasks, while tree ensemble models such as Random Forest and Gradient Boosting demonstrate superior accuracy and consistency for behavior classification on balanced datasets. Our findings emphasize the importance of multi-metric evaluation frameworks and context-aware algorithm selection in the development of scalable and reliable livestock monitoring systems.