UAV-based temporal synergistic estimation of multiple alfalfa qualities integrating physics-informed network and 3D allometric operator
Tao Cheng, Zhangru Gao, Weibo Ren, Ziyu Ding, Feng Yuan, Yaling Liu, Jiyuan Xie, Ruopu Zan, Dong Wang, Dongyan Zhang · 2026
Read UAV-based temporal synergistic estimation of multiple alfalfa qualities integrating physics-informed network and 3D allometric operator by Tao Cheng, Zhangru Gao, Weibo Ren, Ziyu Ding, Feng Yuan, Yaling Liu, Jiyuan Xie, Ruopu Zan, Dong Wang, Dongyan Zhang (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/PMC13325912.txt.
Description
Tao Cheng and co-authors estimate alfalfa quality from drone flights across the growing season in a 2026 study. Feed value hinges on protein and fiber, but single-date images saturate and small samples make models overfit.
A set of 127 core alfalfa lines was scanned, and 14,960 spectral operators were screened for signal. Four-band difference features tracked fiber with a correlation of 0.896 in absolute terms, while log-based features isolated protein at 0.868 on the same scale. A physics-informed sparse network then weighted growth stages under carbon and nitrogen rules. Test accuracy reached R2 values of 0.812 to 0.848 for nitrogen, protein, and fiber traits.
Catalog record
| CREATOR | Tao Cheng, Zhangru Gao, Weibo Ren, Ziyu Ding, Feng Yuan, Yaling Liu, Jiyuan Xie, Ruopu Zan, Dong Wang, Dongyan Zhang |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 117697 |
| LICENSE | https://creativecommons.org/licenses/by/4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |