Sensor-Enabled Automated Bioacoustic Systems for Precision Livestock Management and Wildlife Welfare Monitoring: A Comprehensive Review

Abstract

Acoustic signals play a vital role in animal communication and offer a valuable, non-invasive means of assessing animal health and welfare. Insights from bioacoustic analysis allow livestock farmers to adopt sound-based monitoring systems that improve farm management while minimizing direct interaction with animals. The use of audio and acoustic sensors provides a practical and animal-friendly approach for monitoring large populations, enabling continuous, real-time observation that is less intrusive than conventional physiological methods. However, several challenges persist, including limited availability of high-quality datasets, variability in recording environments, and difficulties in interpreting model outputs, which collectively hinder large-scale deployment in farm settings. To address these limitations, the incorporation of advanced data augmentation strategies and multi-modal sensor integration has been suggested to enhance data diversity and improve classification performance. Current research efforts are focused on developing cost-effective, real-time, and robust bio-acoustic monitoring systems that are adaptable to varying farm conditions and species. Such systems have the potential to transform livestock management by enabling early detection of health and welfare issues, thereby supporting more sustainable and ethically responsible farming practices. This paper provides a comprehensive review of bioacoustic-based approaches for animal welfare monitoring, encompassing a wide range of techniques including individual identification, stress detection, estrus recognition, and respiratory condition analysis. Spanning over a decade of research, the current study underscores the evolving use of machine learning and deep neural networks, including convolutional and transformer-based models, in decoding complex animal vocalizations. This review develops a comprehensive taxonomy and provides a critical synthesis of sensor-enabled bioacoustic systems for livestock and wildlife monitoring. It further outlines major research gaps and practical deployment challenges, proposing a forward-looking framework to facilitate scalable and real-world implementation of these systems.