BiAF: research on dynamic goat herd detection and tracking based on machine vision
Abstract As technology advances, rangeland management is rapidly transitioning toward intelligent systems. To optimize grassland resources and implement scientific grazing practices, livestock grazing monitoring has become a pivotal area of research. Traditional methods, such as manual tracking and...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-02-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-89231-6 |
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| _version_ | 1823862185406758912 |
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| author | Yun Hou Mingjuan Han Wei Fan Xinyu Jia Zhuo Gong Ding Han |
| author_facet | Yun Hou Mingjuan Han Wei Fan Xinyu Jia Zhuo Gong Ding Han |
| author_sort | Yun Hou |
| collection | DOAJ |
| description | Abstract As technology advances, rangeland management is rapidly transitioning toward intelligent systems. To optimize grassland resources and implement scientific grazing practices, livestock grazing monitoring has become a pivotal area of research. Traditional methods, such as manual tracking and wearable monitoring, often disrupt the natural movement and feeding behaviors of grazing livestock, posing significant challenges for in-depth studies of grazing patterns. In this paper, we propose a machine vision-based grazing goat herd detection algorithm that enhances the streamlined ELAN module in YOLOv7-tiny, incorporates an optimized CBAM attention mechanism, refines the SPPCSPC module to reduce the parameter count, and improves the anchor boxes in YOLOv7-tiny to enhance target detection accuracy. The BiAF-YOLOv7 algorithm achieves precision, recall, F1 score, and mAP values of 94.5, 96.7, 94.8, and 96.0%, respectively, on the goat herd dataset. Combined with DeepSORT, our system successfully tracks goat herds, demonstrating the effectiveness of the BiAF-YOLOv7 algorithm as a tool for livestock grazing monitoring. This study not only validates the practicality of the proposed algorithm but also highlights the broader applicability of machine vision-based monitoring in large-scale environments. It provides innovative approaches to achieve grass-animal balance through information-driven methods, such as monitoring and tracking. |
| format | Article |
| id | doaj-art-7864d7eec93146bbaaff7565ebc50de7 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-7864d7eec93146bbaaff7565ebc50de72025-02-09T12:37:27ZengNature PortfolioScientific Reports2045-23222025-02-0115112010.1038/s41598-025-89231-6BiAF: research on dynamic goat herd detection and tracking based on machine visionYun Hou0Mingjuan Han1Wei Fan2Xinyu Jia3Zhuo Gong4Ding Han5College of Electronic Information Engineering, Inner Mongolia UniversityCollege of Electronic Information Engineering, Inner Mongolia UniversityCollege of Electronic Information Engineering, Inner Mongolia UniversityCollege of Electronic Information Engineering, Inner Mongolia UniversityCollege of Electronic Information Engineering, Inner Mongolia UniversityCollege of Electronic Information Engineering, Inner Mongolia UniversityAbstract As technology advances, rangeland management is rapidly transitioning toward intelligent systems. To optimize grassland resources and implement scientific grazing practices, livestock grazing monitoring has become a pivotal area of research. Traditional methods, such as manual tracking and wearable monitoring, often disrupt the natural movement and feeding behaviors of grazing livestock, posing significant challenges for in-depth studies of grazing patterns. In this paper, we propose a machine vision-based grazing goat herd detection algorithm that enhances the streamlined ELAN module in YOLOv7-tiny, incorporates an optimized CBAM attention mechanism, refines the SPPCSPC module to reduce the parameter count, and improves the anchor boxes in YOLOv7-tiny to enhance target detection accuracy. The BiAF-YOLOv7 algorithm achieves precision, recall, F1 score, and mAP values of 94.5, 96.7, 94.8, and 96.0%, respectively, on the goat herd dataset. Combined with DeepSORT, our system successfully tracks goat herds, demonstrating the effectiveness of the BiAF-YOLOv7 algorithm as a tool for livestock grazing monitoring. This study not only validates the practicality of the proposed algorithm but also highlights the broader applicability of machine vision-based monitoring in large-scale environments. It provides innovative approaches to achieve grass-animal balance through information-driven methods, such as monitoring and tracking.https://doi.org/10.1038/s41598-025-89231-6Target detectionDeep learningGoat herd trackingDeepSORTYOLOv7Animal welfare. |
| spellingShingle | Yun Hou Mingjuan Han Wei Fan Xinyu Jia Zhuo Gong Ding Han BiAF: research on dynamic goat herd detection and tracking based on machine vision Scientific Reports Target detection Deep learning Goat herd tracking DeepSORT YOLOv7 Animal welfare. |
| title | BiAF: research on dynamic goat herd detection and tracking based on machine vision |
| title_full | BiAF: research on dynamic goat herd detection and tracking based on machine vision |
| title_fullStr | BiAF: research on dynamic goat herd detection and tracking based on machine vision |
| title_full_unstemmed | BiAF: research on dynamic goat herd detection and tracking based on machine vision |
| title_short | BiAF: research on dynamic goat herd detection and tracking based on machine vision |
| title_sort | biaf research on dynamic goat herd detection and tracking based on machine vision |
| topic | Target detection Deep learning Goat herd tracking DeepSORT YOLOv7 Animal welfare. |
| url | https://doi.org/10.1038/s41598-025-89231-6 |
| work_keys_str_mv | AT yunhou biafresearchondynamicgoatherddetectionandtrackingbasedonmachinevision AT mingjuanhan biafresearchondynamicgoatherddetectionandtrackingbasedonmachinevision AT weifan biafresearchondynamicgoatherddetectionandtrackingbasedonmachinevision AT xinyujia biafresearchondynamicgoatherddetectionandtrackingbasedonmachinevision AT zhuogong biafresearchondynamicgoatherddetectionandtrackingbasedonmachinevision AT dinghan biafresearchondynamicgoatherddetectionandtrackingbasedonmachinevision |