AI-enhanced real-time cattle identification system through tracking across various environments

• Published in: Scientific reports Vol. 14; no. 1; pp. 17779 - 24
• Main Authors: Mon, Su Larb, Onizuka, Tsubasa, Tin, Pyke, Aikawa, Masaru, Kobayashi, Ikuo, Zin, Thi Thi
• Format: Journal Article
• Language: English
• Published: England Nature Publishing Group 01.08.2024; Nature Portfolio
• Subjects: Animal Identification Systems - instrumentation; Animal Identification Systems - methods; Animals; Artificial Intelligence; Cameras; Cattle; Cow's milk; Dairying - methods; Environmental monitoring; Farm management; Female; Identification systems; Image Processing, Computer-Assisted - methods; Livestock farming; Milk production; Milking; Support Vector Machine; Support vector machines; Video Recording - methods
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-024-68418-3

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.