| アイテムタイプ |
学術雑誌論文 / Journal Article(1) |
| 公開日 |
2025-03-25 |
| タイトル |
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タイトル |
AI-enhanced real-time cattle identification system through tracking across various environments |
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言語 |
en |
| 言語 |
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言語 |
eng |
| 資源タイプ |
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資源タイプ |
journal article |
| アクセス権 |
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アクセス権 |
open access |
| 著者 |
Mon, Su Larb
Onizuka, Tsubasa
Tin, Pyke
相川, 勝
WEKO
12201
e-Rad_Researcher
20976641
| ja |
相川, 勝
宮崎大学
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| ja-Kana |
アイカワ, マサル
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| en |
Aikawa, Masaru
University of Miyazaki
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Search repository
小林, 郁雄
WEKO
5214
e-Rad_Researcher
20576293
| ja |
小林, 郁雄
宮崎大学
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| ja-Kana |
コバヤシ, イクオ
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| en |
Kobayashi, Ikuo
University of Miyazaki
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Search repository
ティ ティ ズイン
WEKO
31575
e-Rad_Researcher
30536959
| ja |
ティ ティ ズイン
宮崎大学
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| ja-Kana |
ティ ティ ズイン
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| en |
Thi Thi Zin
University of Miyazaki
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Search repository
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| 抄録 |
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内容記述タイプ |
Abstract |
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内容記述 |
Video-based monitoring is essential nowadays in cattle farm management systems for automated evaluation of cow health, encompassing body condition scores, lameness detection, calving events, and other factors. In order to efficiently monitor the well-being of each individual animal, it is vital to automatically identify them in real time. Although there are various techniques available for cattle identification, a significant number of them depend on radio frequency or visible ear tags, which are prone to being lost or damaged. This can result in financial difficulties for farmers. Therefore, this paper presents a novel method for tracking and identifying the cattle with an RGB image-based camera. As a first step, to detect the cattle in the video, we employ the YOLOv8 (You Only Look Once) model. The sample data contains the raw video that was recorded with the cameras that were installed at above from the designated lane used by cattle after the milk production process and above from the rotating milking parlor. As a second step, the detected cattle are continuously tracked and assigned unique local IDs. The tracked images of each individual cattle are then stored in individual folders according to their respective IDs, facilitating the identification process. The images of each folder will be the features which are extracted using a feature extractor called VGG (Visual Geometry Group). After feature extraction task, as a final step, the SVM (Support Vector Machine) identifier for cattle identification will be used to get the identified ID of the cattle. The final ID of a cattle is determined based on the maximum identified output ID from the tracked images of that particular animal. The outcomes of this paper will act as proof of the concept for the use of combining VGG features with SVM is an effective and promising approach for an automatic cattle identification system |
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言語 |
en |
| 内容記述 |
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内容記述タイプ |
Other |
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内容記述 |
Citation: Su Larb Mon, Tsubasa Onizuka, Pyke Tin, Masaru Aikawa, Ikuo Kobayashi, Thi Thi Zin, AI-enhanced real-time cattle identification system through tracking across various environments, Scientific Reports, 14(1), 2024-08-01, https://doi.org/10.1038/s41598-024-68418-3 |
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言語 |
en |
| bibliographic_information |
en : Scientific Reports
巻 14,
号 1,
発行日 2024-08-01
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| 出版者 |
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出版者 |
Springer Science and Business Media LLC |
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言語 |
en |
| ISSN |
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収録物識別子タイプ |
EISSN |
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収録物識別子 |
2045-2322 |
| item_10001_relation_14 |
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関連タイプ |
isVersionOf |
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識別子タイプ |
DOI |
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関連識別子 |
https://doi.org/10.1038/s41598-024-68418-3 |
| 権利 |
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権利情報 |
© Te Author(s) 2024 |
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言語 |
en |
| 出版タイプ |
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出版タイプ |
VoR |
article (4.8 MB)
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