A UAV-based semi-supervised segmentation framework for pine wilt disease via vegetation Index–RGB synergy
Tao Liu, Wentao Peng, Huaiqing Zhang, Hui Lin, Langwen Tang, Jingdong Li, Xinyu Zhang, Chaoying He, Zilin Ye, Yingfang Zhu, Guoxiong Zhou, Shichao Jin · 2026
Read A UAV-based semi-supervised segmentation framework for pine wilt disease via vegetation Index–RGB synergy by Tao Liu, Wentao Peng, Huaiqing Zhang, Hui Lin, Langwen Tang, Jingdong Li, Xinyu Zhang, Chaoying He, Zilin Ye, Yingfang Zhu, Guoxiong Zhou, Shichao Jin (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/PMC13452197.txt.
Description
Tao Liu and a very large team build in 2026 a drone vision frame that spots pine wilt disease while learning from few hand-marked photos. The disease, driven by a tiny worm, kills pines fast and threatens whole forests. Hand marking of canopy photos is costly, sick crowns change look as the disease climbs, and forest backdrops mimic sick zones.
Their frame pairs a teacher-student setup that keeps stage-wise memory with a check that trusts self-made labels only when they look firm. A cue stream from plant health indices steers the visual read. A shared drone data set for the disease anchors the work. The aim is early spotting from the air at survey scale.
Catalog record
| CREATOR | Tao Liu, Wentao Peng, Huaiqing Zhang, Hui Lin, Langwen Tang, Jingdong Li, Xinyu Zhang, Chaoying He, Zilin Ye, Yingfang Zhu, Guoxiong Zhou, Shichao Jin |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 80999 |
| LICENSE | https://creativecommons.org/licenses/by/4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |