Classification and Recognition of Goat Movement Behavior Based on SL-WOA-XGBoost
Aiming at the problem of time-consuming, labor-intensive, and low-accuracy monitoring of goat motion behavior (lying, standing, walking, and running) while relying on the three-axis acceleration sensor and taking the acceleration data obtained from the goat back collection point as the research obje...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-08-01
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| Series: | Electronics |
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| Online Access: | https://www.mdpi.com/2079-9292/12/16/3506 |
| _version_ | 1797584915980615680 |
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| author | Tingxia Li Tiankai Li Rina Su Jile Xin Ding Han |
| author_facet | Tingxia Li Tiankai Li Rina Su Jile Xin Ding Han |
| author_sort | Tingxia Li |
| collection | DOAJ |
| description | Aiming at the problem of time-consuming, labor-intensive, and low-accuracy monitoring of goat motion behavior (lying, standing, walking, and running) while relying on the three-axis acceleration sensor and taking the acceleration data obtained from the goat back collection point as the research object, a method based on social learning (SL) is proposed using the Whale Optimization Algorithm (WOA) and XGBoost for goat motion behavior recognition. In this method, the XGBoost parameters are optimized by the WOA combined with social learning strategies to improve the classification and recognition accuracy. The results show that the recognition rate of lying behavior was as high as 97.14%, and the average recognition rate of the four movement behaviors was 94.42%, meeting the requirements of goat motion behavior recognition. Compared with the conventional XGBoost algorithm, the average recognition rate was increased by 3.41% and the recognition accuracy was improved. The results of this study can provide a reference for goat health assessment and intelligent disease warning. |
| first_indexed | 2024-03-10T23:59:29Z |
| format | Article |
| id | doaj.art-aef1754edac24e268d835e3775b6c62d |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-10T23:59:29Z |
| publishDate | 2023-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-aef1754edac24e268d835e3775b6c62d2023-11-19T00:54:38ZengMDPI AGElectronics2079-92922023-08-011216350610.3390/electronics12163506Classification and Recognition of Goat Movement Behavior Based on SL-WOA-XGBoostTingxia Li0Tiankai Li1Rina Su2Jile Xin3Ding Han4College of Electronic Information Engineering, Inner Mongolia University, Hohhot 010021, ChinaInner Mongolia Electric Power Survey and Design Institute Co., Ltd., Hohhot 010021, ChinaEtuoke Banner Brand Promotion Service Center in Etuoke Banner Agriculture and Animal Husbandry Bureau, Ordos 010300, ChinaCollege of Electronic Information Engineering, Inner Mongolia University, Hohhot 010021, ChinaCollege of Electronic Information Engineering, Inner Mongolia University, Hohhot 010021, ChinaAiming at the problem of time-consuming, labor-intensive, and low-accuracy monitoring of goat motion behavior (lying, standing, walking, and running) while relying on the three-axis acceleration sensor and taking the acceleration data obtained from the goat back collection point as the research object, a method based on social learning (SL) is proposed using the Whale Optimization Algorithm (WOA) and XGBoost for goat motion behavior recognition. In this method, the XGBoost parameters are optimized by the WOA combined with social learning strategies to improve the classification and recognition accuracy. The results show that the recognition rate of lying behavior was as high as 97.14%, and the average recognition rate of the four movement behaviors was 94.42%, meeting the requirements of goat motion behavior recognition. Compared with the conventional XGBoost algorithm, the average recognition rate was increased by 3.41% and the recognition accuracy was improved. The results of this study can provide a reference for goat health assessment and intelligent disease warning.https://www.mdpi.com/2079-9292/12/16/3506behavior recognitiongoatsocial learningWhale Optimization AlgorithmXGBoost |
| spellingShingle | Tingxia Li Tiankai Li Rina Su Jile Xin Ding Han Classification and Recognition of Goat Movement Behavior Based on SL-WOA-XGBoost Electronics behavior recognition goat social learning Whale Optimization Algorithm XGBoost |
| title | Classification and Recognition of Goat Movement Behavior Based on SL-WOA-XGBoost |
| title_full | Classification and Recognition of Goat Movement Behavior Based on SL-WOA-XGBoost |
| title_fullStr | Classification and Recognition of Goat Movement Behavior Based on SL-WOA-XGBoost |
| title_full_unstemmed | Classification and Recognition of Goat Movement Behavior Based on SL-WOA-XGBoost |
| title_short | Classification and Recognition of Goat Movement Behavior Based on SL-WOA-XGBoost |
| title_sort | classification and recognition of goat movement behavior based on sl woa xgboost |
| topic | behavior recognition goat social learning Whale Optimization Algorithm XGBoost |
| url | https://www.mdpi.com/2079-9292/12/16/3506 |
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