Detectability of multi-dimensional movement and behaviour in cattle using sensor data and machine learning algorithms: Study on a Charolais bull
The development of motion sensors for monitoring cattle behaviour has enabled farmers to predict the state of their cattle's welfare more efficiently. While most studies work with one dimensional output with disjunct behaviour categories, more accurate prediction can still be achieved by includ...
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| Format: | Article |
| Language: | English |
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KeAi Communications Co., Ltd.
2024-12-01
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| Series: | Artificial Intelligence in Agriculture |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2589721724000412 |
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| author | Miklós Biszkup Gábor Vásárhelyi Nuri Nurlaila Setiawan Aliz Márton Szilárd Szentes Petra Balogh Barbara Babay-Török Gábor Pajor Dóra Drexler |
| author_facet | Miklós Biszkup Gábor Vásárhelyi Nuri Nurlaila Setiawan Aliz Márton Szilárd Szentes Petra Balogh Barbara Babay-Török Gábor Pajor Dóra Drexler |
| author_sort | Miklós Biszkup |
| collection | DOAJ |
| description | The development of motion sensors for monitoring cattle behaviour has enabled farmers to predict the state of their cattle's welfare more efficiently. While most studies work with one dimensional output with disjunct behaviour categories, more accurate prediction can still be achieved by including complex movements and enriching the sensor algorithm to detect multi-dimensional movements, i.e., more than one movement occurring simultaneously. This paper presents such a machine-learning method for analysing overlapping independent movements. The output of the method consists of automatically recognized complex behaviour patterns that can be used for measuring animal welfare, predicting calving, or detecting early signs of diseases. This study combines automated motion sensors (i.e., halter and pedometer) for ruminants known as RumiWatch mounted on a Charolais fattening bull and camera observation. Fourteen types of complex movements were identified, i.e., defecating-urinating, eating, drinking, getting up, head movement, licking, lying down, lying, playing-aggression, rubbing, ruminating, sleeping, standing, and stepping. As multiple parallel binary classificators were used, the system was able to recognize parallel behavioural patterns with high fidelity. Two types of machine learning, i.e., Support Vector Classification (SVC) and RandomForest were used to recognize different general and non-general forms of movement. Results from these two supervised learning systems were compared. A continuous forty-eight hours of video were annotated to train the systems and validate their predictions. The success rate of both classifiers in recognizing special movements from both sensors or separately in different settings (i.e., window and padding) was examined. Although the two classifiers produced different results, the ideal settings showed that all forms of movement in the subject animal were successfully recognized with high accuracy. More studies using more individual animals and different ruminants would increase our knowledge on enhancing the system's performance and accuracy. |
| format | Article |
| id | doaj-art-ee2d7d81e26e451bb3843de1a68cc074 |
| institution | Kabale University |
| issn | 2589-7217 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | Artificial Intelligence in Agriculture |
| spelling | doaj-art-ee2d7d81e26e451bb3843de1a68cc0742024-12-14T06:31:55ZengKeAi Communications Co., Ltd.Artificial Intelligence in Agriculture2589-72172024-12-01148698Detectability of multi-dimensional movement and behaviour in cattle using sensor data and machine learning algorithms: Study on a Charolais bullMiklós Biszkup0Gábor Vásárhelyi1Nuri Nurlaila Setiawan2Aliz Márton3Szilárd Szentes4Petra Balogh5Barbara Babay-Török6Gábor Pajor7Dóra Drexler8Hungarian Research Institute of Organic Agriculture, Budapest, Hungary; Corresponding author.CollMot Robotics Ltd., Budapest, Hungary; Eötvös University, Department of Biological Physics, Budapest, HungaryHungarian Research Institute of Organic Agriculture, Budapest, HungaryHungarian Research Institute of Organic Agriculture, Budapest, HungaryUniversity of Veterinary Medicine, Animal Breeding, Nutrition and Laboratory Animal Science Department, Budapest, HungaryHungarian Research Institute of Organic Agriculture, Budapest, HungaryHungarian Research Institute of Organic Agriculture, Budapest, HungaryHungarian Research Institute of Organic Agriculture, Budapest, HungaryHungarian Research Institute of Organic Agriculture, Budapest, HungaryThe development of motion sensors for monitoring cattle behaviour has enabled farmers to predict the state of their cattle's welfare more efficiently. While most studies work with one dimensional output with disjunct behaviour categories, more accurate prediction can still be achieved by including complex movements and enriching the sensor algorithm to detect multi-dimensional movements, i.e., more than one movement occurring simultaneously. This paper presents such a machine-learning method for analysing overlapping independent movements. The output of the method consists of automatically recognized complex behaviour patterns that can be used for measuring animal welfare, predicting calving, or detecting early signs of diseases. This study combines automated motion sensors (i.e., halter and pedometer) for ruminants known as RumiWatch mounted on a Charolais fattening bull and camera observation. Fourteen types of complex movements were identified, i.e., defecating-urinating, eating, drinking, getting up, head movement, licking, lying down, lying, playing-aggression, rubbing, ruminating, sleeping, standing, and stepping. As multiple parallel binary classificators were used, the system was able to recognize parallel behavioural patterns with high fidelity. Two types of machine learning, i.e., Support Vector Classification (SVC) and RandomForest were used to recognize different general and non-general forms of movement. Results from these two supervised learning systems were compared. A continuous forty-eight hours of video were annotated to train the systems and validate their predictions. The success rate of both classifiers in recognizing special movements from both sensors or separately in different settings (i.e., window and padding) was examined. Although the two classifiers produced different results, the ideal settings showed that all forms of movement in the subject animal were successfully recognized with high accuracy. More studies using more individual animals and different ruminants would increase our knowledge on enhancing the system's performance and accuracy.http://www.sciencedirect.com/science/article/pii/S2589721724000412CattlePLFMotion sensorsRumiWatchComplex behaviourMulti-dimensional |
| spellingShingle | Miklós Biszkup Gábor Vásárhelyi Nuri Nurlaila Setiawan Aliz Márton Szilárd Szentes Petra Balogh Barbara Babay-Török Gábor Pajor Dóra Drexler Detectability of multi-dimensional movement and behaviour in cattle using sensor data and machine learning algorithms: Study on a Charolais bull Artificial Intelligence in Agriculture Cattle PLF Motion sensors RumiWatch Complex behaviour Multi-dimensional |
| title | Detectability of multi-dimensional movement and behaviour in cattle using sensor data and machine learning algorithms: Study on a Charolais bull |
| title_full | Detectability of multi-dimensional movement and behaviour in cattle using sensor data and machine learning algorithms: Study on a Charolais bull |
| title_fullStr | Detectability of multi-dimensional movement and behaviour in cattle using sensor data and machine learning algorithms: Study on a Charolais bull |
| title_full_unstemmed | Detectability of multi-dimensional movement and behaviour in cattle using sensor data and machine learning algorithms: Study on a Charolais bull |
| title_short | Detectability of multi-dimensional movement and behaviour in cattle using sensor data and machine learning algorithms: Study on a Charolais bull |
| title_sort | detectability of multi dimensional movement and behaviour in cattle using sensor data and machine learning algorithms study on a charolais bull |
| topic | Cattle PLF Motion sensors RumiWatch Complex behaviour Multi-dimensional |
| url | http://www.sciencedirect.com/science/article/pii/S2589721724000412 |
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