Cattle behaviour classification from collar, halter, and ear tag sensors
In this paper, we summarise the outcome of a set of experiments aimed at classifying cattle behaviour based on sensor data. Each animal carried sensors generating time series accelerometer data placed on a collar on the neck at the back of the head, on a halter positioned at the side of the head beh...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2018-03-01
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| Series: | Information Processing in Agriculture |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2214317317301099 |
| _version_ | 1797700044403507200 |
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| author | A. Rahman D.V. Smith B. Little A.B. Ingham P.L. Greenwood G.J. Bishop-Hurley |
| author_facet | A. Rahman D.V. Smith B. Little A.B. Ingham P.L. Greenwood G.J. Bishop-Hurley |
| author_sort | A. Rahman |
| collection | DOAJ |
| description | In this paper, we summarise the outcome of a set of experiments aimed at classifying cattle behaviour based on sensor data. Each animal carried sensors generating time series accelerometer data placed on a collar on the neck at the back of the head, on a halter positioned at the side of the head behind the mouth, or on the ear using a tag. The purpose of the study was to determine how sensor data from different placement can classify a range of typical cattle behaviours. Data were collected and animal behaviours (grazing, standing or ruminating) were observed over a common time frame. Statistical features were computed from the sensor data and machine learning algorithms were trained to classify each behaviour. Classification accuracies were computed on separate independent test sets. The analysis based on behaviour classification experiments revealed that different sensor placement can achieve good classification accuracy if the feature space (representing motion patterns) between the training and test animal is similar. The paper will discuss these analyses in detail and can act as a guide for future studies. |
| first_indexed | 2024-03-12T04:16:29Z |
| format | Article |
| id | doaj.art-d3fb55cad7534531932e13d72b5d1c12 |
| institution | Directory Open Access Journal |
| issn | 2214-3173 |
| language | English |
| last_indexed | 2024-03-12T04:16:29Z |
| publishDate | 2018-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Information Processing in Agriculture |
| spelling | doaj.art-d3fb55cad7534531932e13d72b5d1c122023-09-03T10:37:10ZengElsevierInformation Processing in Agriculture2214-31732018-03-015112413310.1016/j.inpa.2017.10.001Cattle behaviour classification from collar, halter, and ear tag sensorsA. Rahman0D.V. Smith1B. Little2A.B. Ingham3P.L. Greenwood4G.J. Bishop-Hurley5Analytics Program, Data61, CSIRO, AustraliaAnalytics Program, Data61, CSIRO, AustraliaProductive and Adaptive Livestock Systems, Agriculture and Food, CSIRO, AustraliaProductive and Adaptive Livestock Systems, Agriculture and Food, CSIRO, AustraliaNSW Department of Primary Industries Beef Industry Centre, AustraliaProductive and Adaptive Livestock Systems, Agriculture and Food, CSIRO, AustraliaIn this paper, we summarise the outcome of a set of experiments aimed at classifying cattle behaviour based on sensor data. Each animal carried sensors generating time series accelerometer data placed on a collar on the neck at the back of the head, on a halter positioned at the side of the head behind the mouth, or on the ear using a tag. The purpose of the study was to determine how sensor data from different placement can classify a range of typical cattle behaviours. Data were collected and animal behaviours (grazing, standing or ruminating) were observed over a common time frame. Statistical features were computed from the sensor data and machine learning algorithms were trained to classify each behaviour. Classification accuracies were computed on separate independent test sets. The analysis based on behaviour classification experiments revealed that different sensor placement can achieve good classification accuracy if the feature space (representing motion patterns) between the training and test animal is similar. The paper will discuss these analyses in detail and can act as a guide for future studies.http://www.sciencedirect.com/science/article/pii/S2214317317301099Sensor data analyticsCattle behaviour classificationSensors for cattle behaviour tracking |
| spellingShingle | A. Rahman D.V. Smith B. Little A.B. Ingham P.L. Greenwood G.J. Bishop-Hurley Cattle behaviour classification from collar, halter, and ear tag sensors Information Processing in Agriculture Sensor data analytics Cattle behaviour classification Sensors for cattle behaviour tracking |
| title | Cattle behaviour classification from collar, halter, and ear tag sensors |
| title_full | Cattle behaviour classification from collar, halter, and ear tag sensors |
| title_fullStr | Cattle behaviour classification from collar, halter, and ear tag sensors |
| title_full_unstemmed | Cattle behaviour classification from collar, halter, and ear tag sensors |
| title_short | Cattle behaviour classification from collar, halter, and ear tag sensors |
| title_sort | cattle behaviour classification from collar halter and ear tag sensors |
| topic | Sensor data analytics Cattle behaviour classification Sensors for cattle behaviour tracking |
| url | http://www.sciencedirect.com/science/article/pii/S2214317317301099 |
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