Machine learning-assisted self-powered ear tag for animal welfare
Xiaoyu Su, Peidi Fan, Ying Liu, Jianfeng Ping, Xunjia Li, Yuxiang Pan · 2026
Read Machine learning-assisted self-powered ear tag for animal welfare by Xiaoyu Su, Peidi Fan, Ying Liu, Jianfeng Ping, Xunjia Li, Yuxiang Pan (2026) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/pmc-13392078.txt.
Description
Xiaoyu Su's team built a self-powered, machine-learning ear tag that tracks farm-animal energy-use-linked welfare through between-cell fluid ions. Heavy farming diets often disturb energy use, yet spotting off animals at scale is hard.
A friction-plus-solar gatherer with on-off operation powers micro-needle sensors tracking pH, potassium, and calcium over time. A machine-learning chain reading joint ion dynamics told apart five welfare-related states at over ninety-five percent average hit rate, checked across 5,399 sample windows from three animals, establishing a large-scale precision-farm-animal model. The tag tracked pH, potassium, and calcium over time.
Catalog record
| CREATOR | Xiaoyu Su, Peidi Fan, Ying Liu, Jianfeng Ping, Xunjia Li, Yuxiang Pan |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 47 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |