Giving Cows a Digital Voice: AI-Enabled Bioacoustics and Smart Sensing in Precision Livestock Management – A Review

A critical review of AI techniques for decoding cattle vocalizations, proposing the Hybrid Explainable Acoustic Multimodal (HEAM) model for transparent, real-time welfare assessment.

Mayuri Kate; S. Neethirajan· Annals of Animal Science· 2025· DOI 10.2478/aoas-2025-0091

The core problem

Cattle vocalizations encode physiological and emotional states, often manifesting before visible behavioral symptoms. This review critically examines the progression of artificial intelligence (AI) techniques used to decode these vocal signals, from early signal processing and classical machine learning to contemporary deep learning architectures and large language models (LLMs). Drawing from a systematic analysis of over 120 core studies, the authors evaluate the capabilities, limitations, and real-world applicability of current methods. The central premise is that continuous, sensor-driven, animal-centered analysis can shift welfare monitoring from intermittent human observation to real-time, context-aware assessment, enabling earlier disease detection, improved treatment outcomes, enhanced productivity, and increased societal trust in precision livestock farming. The review also addresses ethical considerations such as anthropomorphism, data privacy, and potential misuse of AI in welfare decisions, and outlines best practices for dataset curation, cross-farm validation, and model explainability.

The review reports that AI-enabled bioacoustics has advanced significantly, with deep learning architectures outperforming classical methods in decoding cattle vocalizations. However, performance often degrades when models are applied across different farms due to data scarcity and domain shift. Multimodal integration—combining audio with accelerometry, thermal imaging, and environmental inputs—emerges as a pivotal strategy for achieving accurate, context-aware, and real-time welfare assessment. The proposed HEAM model fuses spectrogram-based CNNs, interpretable decision trees, and natural language reasoning modules to generate transparent alerts. Key findings include: - **Data scarcity**: Limited labeled datasets constrain model training and generalization. - **Cross-farm generalizability**: Models trained on one farm often fail on another. - **Interpretability**: Black-box models lack transparency, hindering farmer trust and adoption. - **Multimodal fusion**: Integrating multiple sensor modalities improves robustness and context-awareness. - **Ethical considerations**: Anthropomorphism, data privacy, and potential misuse of AI in welfare decisions require attention. The review

Cattle vocalizations encode physiological and emotional states, often manifesting before visible behavioral symptoms. This review critically examines the progression of artificial intelligence (AI) techniques used to decode these vocal signals, from early signal processing and classical machine learning to contemporary deep learning architectures and large language models (LLMs). Drawing from a systematic analysis of over 120 core studies, the authors evaluate the capabilities, limitations, and real-world applicability of current methods. The central premise is that continuous, sensor-driven, animal-centered analysis can shift welfare monitoring from intermittent human observation to real-time, context-aware assessment, enabling earlier disease detection, improved treatment outcomes, enhanced productivity, and increased societal trust in precision livestock farming. The review also addresses ethical considerations such as anthropomorphism, data privacy, and potential misuse of AI in welfare decisions, and outlines best practices for dataset curation, cross-farm validation, and model explainability.

The review is based on a systematic analysis of over 120 core studies spanning early signal processing, classical machine learning, deep learning, and LLMs. The authors synthesize findings across acoustic feature extraction, model architectures, and multimodal sensor integration. Key methodological dimensions include:

The authors argue that shifting from intermittent human observation to continuous, sensor-driven, animal-centered analysis is essential for earlier disease detection and improved welfare outcomes. The HEAM model represents a step toward explainable AI in precision livestock farming, but challenges remain. Data scarcity and cross-farm generalizability require standardized dataset curation and validation protocols. Interpretability is critical for farmer trust and ethical deployment. The review discusses the risk of anthropomorphism—attributing human emotions to cattle vocalizations—and the need for careful interpretation. Data privacy and potential misuse of AI in welfare decisions are also highlighted. The authors propose best practices for dataset curation, cross-farm validation, and model explainability. They conclude that AI-enabled bioacoustics holds promise for enhanced productivity and societal trust, but only if technical and ethical challenges are addressed. Future work should focus on multimodal fusion, real-time deployment, and transparent reasoning. The HEAM architecture is presented as a blueprint for integrating CNNs, decision trees, and natural language reasoning to generate actionable alerts. The review calls for interdisciplinary collaboration among animal scientists, AI researchers, and ethicists to ensure responsible innovation.

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