Identification of oestrus cows based on vocalisation characteristics and machine learning technique using a dual-channel-equipped acoustic tag
Timely and accurate detection of oestrus in cows is an essential element of the good management of dairy farms. At present, the detection of cows in oestrus by acoustic means is impeded by the problems of filtering, incomplete feature selection, and poor recognition accuracy. To overcome these diffi...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-06-01
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| Series: | Animal |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1751731123001076 |
| _version_ | 1827924564066172928 |
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| author | Jun Wang Haoran Chen Jianping Wang Kaixuan Zhao Xiaoxia Li Bo Liu Yu Zhou |
| author_facet | Jun Wang Haoran Chen Jianping Wang Kaixuan Zhao Xiaoxia Li Bo Liu Yu Zhou |
| author_sort | Jun Wang |
| collection | DOAJ |
| description | Timely and accurate detection of oestrus in cows is an essential element of the good management of dairy farms. At present, the detection of cows in oestrus by acoustic means is impeded by the problems of filtering, incomplete feature selection, and poor recognition accuracy. To overcome these difficulties, this study proposes a sound detection method for cows in oestrus based on machine learning technology using an optimal feature combination and an optimal time window. Firstly, a dual-channel sound detection tag consisting of a unidirectional microphone and an omnidirectional microphone (OM) was developed. The Least Mean Squares adaptive algorithm based on wavelet thresholds was used to filter the signals from the OM, and the dual-channel endpoint detection algorithm was used to identify the lowing of individual cows. The Friedman analysis was then used to select the sound features with significant differences before and after oestrus in terms of time, frequency, and cepstrum, and these were used to determine the most acceptable feature combination. We then analysed the effects of Back Propagation Neural Network (BPNN), Cartesian Regression Tree, Support Vector Machine, and Random Forest classification on the accuracy, precision, sensitivity, specificity, and F1 score of oestrus discrimination. Different time windows were used, and the discrimination performance of these algorithms was evaluated using the Area under Receiver Operating Characteristic Curve to find the most satisfactory match between the time window and the recognition algorithm. The dual-channel acoustic tag’s accuracy, precision, sensitivity, and specificity results were 91.25, 98.83, 91.75, and 83.68%, respectively. BPNN with the 70 ms time window and the feature combination (spectral roll-off + spectral flatness + Mel-Frequency Cepstrum Coefficients) was confirmed as the most suitable oestrus recognition method. The average accuracy, precision, sensitivity, specificity, and F1 score of this method were 97.62, 98.07, 97.17, 97.19, and 97.63%, respectively. Based on these results, the approach was shown to be a feasible means of oestrus detection in dairy cows. Based on its ability to differentiate cows and its consistency, it was demonstrated that sound has the potential to replace accelerometers as an early indicator of oestrus in dairy cows. |
| first_indexed | 2024-03-13T05:12:24Z |
| format | Article |
| id | doaj.art-caf7315e9ac74f4bb9cf98aa729d5f21 |
| institution | Directory Open Access Journal |
| issn | 1751-7311 |
| language | English |
| last_indexed | 2024-03-13T05:12:24Z |
| publishDate | 2023-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Animal |
| spelling | doaj.art-caf7315e9ac74f4bb9cf98aa729d5f212023-06-16T05:09:03ZengElsevierAnimal1751-73112023-06-01176100811Identification of oestrus cows based on vocalisation characteristics and machine learning technique using a dual-channel-equipped acoustic tagJun Wang0Haoran Chen1Jianping Wang2Kaixuan Zhao3Xiaoxia Li4Bo Liu5Yu Zhou6School of Information Engineering, Henan University of Science and Technology, Luoyang, Henan 471003, PR China; Corresponding author.School of Information Engineering, Henan University of Science and Technology, Luoyang, Henan 471003, PR ChinaSchool of Animal Science and Technology, Henan University of Science and Technology, Luoyang, Henan 471003, PR ChinaSchool of Agricultural Equipment Engineering, Henan University of Science and Technology, Luoyang, Henan 471003, PR ChinaSchool of Animal Science and Technology, Henan University of Science and Technology, Luoyang, Henan 471003, PR ChinaSchool of Information Engineering, Henan University of Science and Technology, Luoyang, Henan 471003, PR ChinaSchool of Medical Technology and Engineering, Henan University of Science and Technology, Luoyang, Henan 471003, PR ChinaTimely and accurate detection of oestrus in cows is an essential element of the good management of dairy farms. At present, the detection of cows in oestrus by acoustic means is impeded by the problems of filtering, incomplete feature selection, and poor recognition accuracy. To overcome these difficulties, this study proposes a sound detection method for cows in oestrus based on machine learning technology using an optimal feature combination and an optimal time window. Firstly, a dual-channel sound detection tag consisting of a unidirectional microphone and an omnidirectional microphone (OM) was developed. The Least Mean Squares adaptive algorithm based on wavelet thresholds was used to filter the signals from the OM, and the dual-channel endpoint detection algorithm was used to identify the lowing of individual cows. The Friedman analysis was then used to select the sound features with significant differences before and after oestrus in terms of time, frequency, and cepstrum, and these were used to determine the most acceptable feature combination. We then analysed the effects of Back Propagation Neural Network (BPNN), Cartesian Regression Tree, Support Vector Machine, and Random Forest classification on the accuracy, precision, sensitivity, specificity, and F1 score of oestrus discrimination. Different time windows were used, and the discrimination performance of these algorithms was evaluated using the Area under Receiver Operating Characteristic Curve to find the most satisfactory match between the time window and the recognition algorithm. The dual-channel acoustic tag’s accuracy, precision, sensitivity, and specificity results were 91.25, 98.83, 91.75, and 83.68%, respectively. BPNN with the 70 ms time window and the feature combination (spectral roll-off + spectral flatness + Mel-Frequency Cepstrum Coefficients) was confirmed as the most suitable oestrus recognition method. The average accuracy, precision, sensitivity, specificity, and F1 score of this method were 97.62, 98.07, 97.17, 97.19, and 97.63%, respectively. Based on these results, the approach was shown to be a feasible means of oestrus detection in dairy cows. Based on its ability to differentiate cows and its consistency, it was demonstrated that sound has the potential to replace accelerometers as an early indicator of oestrus in dairy cows.http://www.sciencedirect.com/science/article/pii/S1751731123001076Feature combinationOestrus detectionRecognition algorithmSoundTime window |
| spellingShingle | Jun Wang Haoran Chen Jianping Wang Kaixuan Zhao Xiaoxia Li Bo Liu Yu Zhou Identification of oestrus cows based on vocalisation characteristics and machine learning technique using a dual-channel-equipped acoustic tag Animal Feature combination Oestrus detection Recognition algorithm Sound Time window |
| title | Identification of oestrus cows based on vocalisation characteristics and machine learning technique using a dual-channel-equipped acoustic tag |
| title_full | Identification of oestrus cows based on vocalisation characteristics and machine learning technique using a dual-channel-equipped acoustic tag |
| title_fullStr | Identification of oestrus cows based on vocalisation characteristics and machine learning technique using a dual-channel-equipped acoustic tag |
| title_full_unstemmed | Identification of oestrus cows based on vocalisation characteristics and machine learning technique using a dual-channel-equipped acoustic tag |
| title_short | Identification of oestrus cows based on vocalisation characteristics and machine learning technique using a dual-channel-equipped acoustic tag |
| title_sort | identification of oestrus cows based on vocalisation characteristics and machine learning technique using a dual channel equipped acoustic tag |
| topic | Feature combination Oestrus detection Recognition algorithm Sound Time window |
| url | http://www.sciencedirect.com/science/article/pii/S1751731123001076 |
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