Classifying vocal responses of broilers to environmental stressors via artificial neural network
Detecting early-stage stress in broiler farms is crucial for optimising growth rates and animal well-being. This study aims to classify various stress calls in broilers exposed to cold, heat, or wind, using acoustic signal processing and a transformer artificial neural network (ANN). Two consecutive...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-01-01
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| Series: | Animal |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S175173112400315X |
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| author | T. Lev-ron Y. Yitzhaky I. Halachmi S. Druyan |
| author_facet | T. Lev-ron Y. Yitzhaky I. Halachmi S. Druyan |
| author_sort | T. Lev-ron |
| collection | DOAJ |
| description | Detecting early-stage stress in broiler farms is crucial for optimising growth rates and animal well-being. This study aims to classify various stress calls in broilers exposed to cold, heat, or wind, using acoustic signal processing and a transformer artificial neural network (ANN). Two consecutive trials were conducted with varying amounts of collected data, and three ANN models with the same architecture but different parameters were examined. The impacts of adding broiler age data as an input attribute and varying input audio waveform lengths on model performance were assessed. Model performance improved with the inclusion of broiler age and longer audio waveforms when trained on smaller datasets. Additionally, the study evaluated the impact of majority vote decision-making across the three ANN model sizes, showing improvement in mean average precision (mAP), particularly for models with shorter audio inputs. Overall, the largest ANN model achieved the highest mAP score of 0.97 for the larger dataset, with small variations among different model sizes. These findings highlight the potential of using a single model to accurately classify multiple types of broiler stress calls. By enhancing the timing of human intervention during critical growth stages, the proposed method may significantly improve broiler welfare and farm management efficiency. |
| format | Article |
| id | doaj-art-6a61592aa69d4edd8bb851ff6d66cc97 |
| institution | Kabale University |
| issn | 1751-7311 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Animal |
| spelling | doaj-art-6a61592aa69d4edd8bb851ff6d66cc972025-01-19T06:24:48ZengElsevierAnimal1751-73112025-01-01191101378Classifying vocal responses of broilers to environmental stressors via artificial neural networkT. Lev-ron0Y. Yitzhaky1I. Halachmi2S. Druyan3School of Electrical and Computer Engineering, Ben-Gurion University of the Negev, 1 Ben Gurion Avenue, P.O.B. 653, Be’er Sheva, 8410501, Israel; Precision Livestock Farming (PLF) Lab, Institute of Agricultural Engineering, Agricultural Research Organization (A.R.O.) – The Volcani Center, 68 Hamaccabim Road, P.O.B 15159 Rishon Lezion, 7505101, Israel; Animal Science Institute, Agricultural Research Organization (A.R.O.) – The Volcani Center, 68 Hamaccabim Road, P.O.B 7505101 Rishon Lezion, 7505101, IsraelSchool of Electrical and Computer Engineering, Ben-Gurion University of the Negev, 1 Ben Gurion Avenue, P.O.B. 653, Be’er Sheva, 8410501, IsraelPrecision Livestock Farming (PLF) Lab, Institute of Agricultural Engineering, Agricultural Research Organization (A.R.O.) – The Volcani Center, 68 Hamaccabim Road, P.O.B 15159 Rishon Lezion, 7505101, IsraelAnimal Science Institute, Agricultural Research Organization (A.R.O.) – The Volcani Center, 68 Hamaccabim Road, P.O.B 7505101 Rishon Lezion, 7505101, Israel; Corresponding author.Detecting early-stage stress in broiler farms is crucial for optimising growth rates and animal well-being. This study aims to classify various stress calls in broilers exposed to cold, heat, or wind, using acoustic signal processing and a transformer artificial neural network (ANN). Two consecutive trials were conducted with varying amounts of collected data, and three ANN models with the same architecture but different parameters were examined. The impacts of adding broiler age data as an input attribute and varying input audio waveform lengths on model performance were assessed. Model performance improved with the inclusion of broiler age and longer audio waveforms when trained on smaller datasets. Additionally, the study evaluated the impact of majority vote decision-making across the three ANN model sizes, showing improvement in mean average precision (mAP), particularly for models with shorter audio inputs. Overall, the largest ANN model achieved the highest mAP score of 0.97 for the larger dataset, with small variations among different model sizes. These findings highlight the potential of using a single model to accurately classify multiple types of broiler stress calls. By enhancing the timing of human intervention during critical growth stages, the proposed method may significantly improve broiler welfare and farm management efficiency.http://www.sciencedirect.com/science/article/pii/S175173112400315XBioacousticsDeep learningEnvironmental monitoringPoultry welfareStress indicators |
| spellingShingle | T. Lev-ron Y. Yitzhaky I. Halachmi S. Druyan Classifying vocal responses of broilers to environmental stressors via artificial neural network Animal Bioacoustics Deep learning Environmental monitoring Poultry welfare Stress indicators |
| title | Classifying vocal responses of broilers to environmental stressors via artificial neural network |
| title_full | Classifying vocal responses of broilers to environmental stressors via artificial neural network |
| title_fullStr | Classifying vocal responses of broilers to environmental stressors via artificial neural network |
| title_full_unstemmed | Classifying vocal responses of broilers to environmental stressors via artificial neural network |
| title_short | Classifying vocal responses of broilers to environmental stressors via artificial neural network |
| title_sort | classifying vocal responses of broilers to environmental stressors via artificial neural network |
| topic | Bioacoustics Deep learning Environmental monitoring Poultry welfare Stress indicators |
| url | http://www.sciencedirect.com/science/article/pii/S175173112400315X |
| work_keys_str_mv | AT tlevron classifyingvocalresponsesofbroilerstoenvironmentalstressorsviaartificialneuralnetwork AT yyitzhaky classifyingvocalresponsesofbroilerstoenvironmentalstressorsviaartificialneuralnetwork AT ihalachmi classifyingvocalresponsesofbroilerstoenvironmentalstressorsviaartificialneuralnetwork AT sdruyan classifyingvocalresponsesofbroilerstoenvironmentalstressorsviaartificialneuralnetwork |