Decoding Poultry Welfare from Sound—A Machine Learning Framework for Non-Invasive Acoustic Monitoring
Acoustic monitoring presents a promising, non-invasive modality for assessing animal welfare in precision livestock farming. In poultry, vocalizations encode biologically relevant cues linked to health status, behavioral states, and environmental stress. This study proposes an integrated analytical...
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MDPI AG
2025-05-01
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| author | Venkatraman Manikandan Suresh Neethirajan |
| author_facet | Venkatraman Manikandan Suresh Neethirajan |
| author_sort | Venkatraman Manikandan |
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| description | Acoustic monitoring presents a promising, non-invasive modality for assessing animal welfare in precision livestock farming. In poultry, vocalizations encode biologically relevant cues linked to health status, behavioral states, and environmental stress. This study proposes an integrated analytical framework that combines signal-level statistical analysis with machine learning and deep learning classifiers to interpret chicken vocalizations in a welfare assessment context. The framework was evaluated using three complementary datasets encompassing health-related vocalizations, behavioral call types, and stress-induced acoustic responses. The pipeline employs a multistage process comprising high-fidelity signal acquisition, feature extraction (e.g., mel-frequency cepstral coefficients, spectral contrast, zero-crossing rate), and classification using models including Random Forest, HistGradientBoosting, CatBoost, TabNet, and LSTM. Feature importance analysis and statistical tests (e.g., <i>t</i>-tests, correlation metrics) confirmed that specific MFCC bands and spectral descriptors were significantly associated with welfare indicators. LSTM-based temporal modeling revealed distinct acoustic trajectories under visual and auditory stress, supporting the presence of habituation and stressor-specific vocal adaptations over time. Model performance, validated through stratified cross-validation and multiple statistical metrics (e.g., F1-score, Matthews correlation coefficient), demonstrated high classification accuracy and generalizability. Importantly, the approach emphasizes model interpretability, facilitating alignment with known physiological and behavioral processes in poultry. The findings underscore the potential of acoustic sensing and interpretable AI as scalable, biologically grounded tools for real-time poultry welfare monitoring, contributing to the advancement of sustainable and ethical livestock production systems. |
| format | Article |
| id | doaj-art-afd6113fb88b419983caccf4b7251dd7 |
| institution | OA Journals |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-05-01 |
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| spelling | doaj-art-afd6113fb88b419983caccf4b7251dd72025-08-20T01:49:18ZengMDPI AGSensors1424-82202025-05-01259291210.3390/s25092912Decoding Poultry Welfare from Sound—A Machine Learning Framework for Non-Invasive Acoustic MonitoringVenkatraman Manikandan0Suresh Neethirajan1Faculty of Computer Science, Dalhousie University, Halifax, NS B3H 4R2, CanadaFaculty of Computer Science, Dalhousie University, Halifax, NS B3H 4R2, CanadaAcoustic monitoring presents a promising, non-invasive modality for assessing animal welfare in precision livestock farming. In poultry, vocalizations encode biologically relevant cues linked to health status, behavioral states, and environmental stress. This study proposes an integrated analytical framework that combines signal-level statistical analysis with machine learning and deep learning classifiers to interpret chicken vocalizations in a welfare assessment context. The framework was evaluated using three complementary datasets encompassing health-related vocalizations, behavioral call types, and stress-induced acoustic responses. The pipeline employs a multistage process comprising high-fidelity signal acquisition, feature extraction (e.g., mel-frequency cepstral coefficients, spectral contrast, zero-crossing rate), and classification using models including Random Forest, HistGradientBoosting, CatBoost, TabNet, and LSTM. Feature importance analysis and statistical tests (e.g., <i>t</i>-tests, correlation metrics) confirmed that specific MFCC bands and spectral descriptors were significantly associated with welfare indicators. LSTM-based temporal modeling revealed distinct acoustic trajectories under visual and auditory stress, supporting the presence of habituation and stressor-specific vocal adaptations over time. Model performance, validated through stratified cross-validation and multiple statistical metrics (e.g., F1-score, Matthews correlation coefficient), demonstrated high classification accuracy and generalizability. Importantly, the approach emphasizes model interpretability, facilitating alignment with known physiological and behavioral processes in poultry. The findings underscore the potential of acoustic sensing and interpretable AI as scalable, biologically grounded tools for real-time poultry welfare monitoring, contributing to the advancement of sustainable and ethical livestock production systems.https://www.mdpi.com/1424-8220/25/9/2912poultry vocalizationsacoustic monitoringanimal welfaremachine learningprecision farmingbioacoustics |
| spellingShingle | Venkatraman Manikandan Suresh Neethirajan Decoding Poultry Welfare from Sound—A Machine Learning Framework for Non-Invasive Acoustic Monitoring Sensors poultry vocalizations acoustic monitoring animal welfare machine learning precision farming bioacoustics |
| title | Decoding Poultry Welfare from Sound—A Machine Learning Framework for Non-Invasive Acoustic Monitoring |
| title_full | Decoding Poultry Welfare from Sound—A Machine Learning Framework for Non-Invasive Acoustic Monitoring |
| title_fullStr | Decoding Poultry Welfare from Sound—A Machine Learning Framework for Non-Invasive Acoustic Monitoring |
| title_full_unstemmed | Decoding Poultry Welfare from Sound—A Machine Learning Framework for Non-Invasive Acoustic Monitoring |
| title_short | Decoding Poultry Welfare from Sound—A Machine Learning Framework for Non-Invasive Acoustic Monitoring |
| title_sort | decoding poultry welfare from sound a machine learning framework for non invasive acoustic monitoring |
| topic | poultry vocalizations acoustic monitoring animal welfare machine learning precision farming bioacoustics |
| url | https://www.mdpi.com/1424-8220/25/9/2912 |
| work_keys_str_mv | AT venkatramanmanikandan decodingpoultrywelfarefromsoundamachinelearningframeworkfornoninvasiveacousticmonitoring AT sureshneethirajan decodingpoultrywelfarefromsoundamachinelearningframeworkfornoninvasiveacousticmonitoring |