AI-Based Intelligent Monitoring System for Estrus Prediction in the Livestock Industry
In order to improve a livestock breeding environment that considers securing safe cattle resources and improving productivity for the intelligent farm, we propose an animal-friendly and worker-friendly intellectual monitoring system with Artificial Intelligent (AI) technology. In order to secure saf...
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-02-01
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| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/13/4/2442 |
| _version_ | 1797622580811661312 |
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| author | Youngjoon Cho Jongwon Kim |
| author_facet | Youngjoon Cho Jongwon Kim |
| author_sort | Youngjoon Cho |
| collection | DOAJ |
| description | In order to improve a livestock breeding environment that considers securing safe cattle resources and improving productivity for the intelligent farm, we propose an animal-friendly and worker-friendly intellectual monitoring system with Artificial Intelligent (AI) technology. In order to secure safe cattle resources and increase productivity for the livestock industry, it is necessary to secure the self-activities of the cattle and predict the estrous state of target cattle as quickly as possible. For the prediction of the estrous state, it is necessary to continuously observe the cattle behavior by workers and quantify the behavior of the target cattle, but that is not easy for workers and needs a long period of continuous observation. We developed the intelligent monitoring system (IMS) with the ARM (Augmented Recognition Model) for the intelligent farm that can predict the estrus of target cattle and get activity data for individual cattle, and then the system was applied to a typical cattle farm for activity monitoring of the Korean cattle (Hanwoo). Therefore, we confirmed the target Hanwoo group with more than 400 activities among the Hanwoo groups using the ARM threshold. Thus, we verified the potential of the proposed system for tracking multiple similar objects. |
| first_indexed | 2024-03-11T09:12:19Z |
| format | Article |
| id | doaj.art-b8906f50c6c44f0dbc920f98ac23a4b4 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-11T09:12:19Z |
| publishDate | 2023-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-b8906f50c6c44f0dbc920f98ac23a4b42023-11-16T18:55:48ZengMDPI AGApplied Sciences2076-34172023-02-01134244210.3390/app13042442AI-Based Intelligent Monitoring System for Estrus Prediction in the Livestock IndustryYoungjoon Cho0Jongwon Kim1Department of Electronics Engineering, Korea University of Technology and Education, Cheonan 31253, Republic of KoreaDepartment of Electromechanical Convergence Engineering, Korea University of Technology and Education, Cheonan 31253, Republic of KoreaIn order to improve a livestock breeding environment that considers securing safe cattle resources and improving productivity for the intelligent farm, we propose an animal-friendly and worker-friendly intellectual monitoring system with Artificial Intelligent (AI) technology. In order to secure safe cattle resources and increase productivity for the livestock industry, it is necessary to secure the self-activities of the cattle and predict the estrous state of target cattle as quickly as possible. For the prediction of the estrous state, it is necessary to continuously observe the cattle behavior by workers and quantify the behavior of the target cattle, but that is not easy for workers and needs a long period of continuous observation. We developed the intelligent monitoring system (IMS) with the ARM (Augmented Recognition Model) for the intelligent farm that can predict the estrus of target cattle and get activity data for individual cattle, and then the system was applied to a typical cattle farm for activity monitoring of the Korean cattle (Hanwoo). Therefore, we confirmed the target Hanwoo group with more than 400 activities among the Hanwoo groups using the ARM threshold. Thus, we verified the potential of the proposed system for tracking multiple similar objects.https://www.mdpi.com/2076-3417/13/4/2442intelligent monitoring systemaugmented recognition modelactivity data acquisitionestrus predictionYOLOv5 |
| spellingShingle | Youngjoon Cho Jongwon Kim AI-Based Intelligent Monitoring System for Estrus Prediction in the Livestock Industry Applied Sciences intelligent monitoring system augmented recognition model activity data acquisition estrus prediction YOLOv5 |
| title | AI-Based Intelligent Monitoring System for Estrus Prediction in the Livestock Industry |
| title_full | AI-Based Intelligent Monitoring System for Estrus Prediction in the Livestock Industry |
| title_fullStr | AI-Based Intelligent Monitoring System for Estrus Prediction in the Livestock Industry |
| title_full_unstemmed | AI-Based Intelligent Monitoring System for Estrus Prediction in the Livestock Industry |
| title_short | AI-Based Intelligent Monitoring System for Estrus Prediction in the Livestock Industry |
| title_sort | ai based intelligent monitoring system for estrus prediction in the livestock industry |
| topic | intelligent monitoring system augmented recognition model activity data acquisition estrus prediction YOLOv5 |
| url | https://www.mdpi.com/2076-3417/13/4/2442 |
| work_keys_str_mv | AT youngjooncho aibasedintelligentmonitoringsystemforestruspredictioninthelivestockindustry AT jongwonkim aibasedintelligentmonitoringsystemforestruspredictioninthelivestockindustry |