AI-Driven Bioacoustics in Poultry Farming: A Critical Systematic Review on Vocalization Analysis for Stress and Disease Detection
Bibliographic record
Abstract
The fusion of artificial intelligence (AI) and acoustic sensing is fundamentally reshaping poultry welfare monitoring by offering unprecedented, non-invasive insights into the emotional, physiological, and behavioral states of birds through vocal analysis. This systematic review delves into the transformative intersection of bioacoustics, machine learning, and animal welfare, meticulously examining the shift from traditional acoustic feature extraction methods, like Mel-Frequency Cepstral Coefficients (MFCCs) and spectrogram analysis, toward cutting-edge deep learning architectures, such as CNNs, LSTMs, attention mechanisms, and powerful self-supervised models like wav2vec2 and Whisper. Critically, it highlights the evolution toward compact, real-time deployment solutions, including TinyML and edge computing, designed specifically for the dynamic and noisy environments of commercial poultry farms. Emotion recognition, early disease detection, and nuanced behavioral decoding emerge as pivotal application areas, underscoring the immense potential for proactive and responsive livestock management. Furthermore, the review sheds light on emerging open-source toolkits and automated pipelines enhancing dataset preprocessing, annotation accuracy, and robust acoustic inference. Employing advanced bibliometric mapping and thematic clustering, the paper identifies critical gaps in research reproducibility, dataset standardization, and the interpretability of complex AI models. It calls for rigorous advancements in explainable AI methodologies, advocates for greater cross-species generalization of acoustic models, and urges a conscientious approach to ethical AI design. Ultimately, this review underscores the necessity for holistic, interpretable, and ethically informed frameworks, positioning acoustic AI as a transformative force in enhancing animal welfare and operational efficiency within the poultry industry.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".