Name
Abstract | ||
|---|---|---|
•A yellow rust detection system was developed by learning from UAV imagery.•RVI, NDVI and OSAVI ranked top 3 among 18 SVIs for rust detection.•NIR and Red ranked top 2 among 5 VIS–NIR bands for rust detection.•An experiment was designed to verify the system with promising performance. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1016/j.compag.2018.10.017 | Computers and Electronics in Agriculture |
Keywords | Field | DocType |
Wheat yellow rust,Multispectral image,Spectral vegetation index (SVI),Unmanned Aerial Vehicle (UAV),Random forest | Computer vision,Monitoring system,Vegetation Index,Multispectral image,Remote sensing,Normalized Difference Vegetation Index,Pixel,Rust,Artificial intelligence,Engineering,Spectral bands,Aerial imagery | Journal |
Volume | ISSN | Citations |
155 | 0168-1699 | 1 |
PageRank | References | Authors |
0.35 | 9 | 9 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jinya Su | 1 | 167 | 14.03 |
| Cunjia Liu | 2 | 60 | 10.48 |
| Matthew Coombes | 3 | 15 | 4.39 |
| Xiaoping Hu | 4 | 5 | 1.11 |
| Conghao Wang | 5 | 1 | 0.35 |
| X Xu | 6 | 6 | 5.20 |
| Qingdong Li | 7 | 102 | 12.25 |
| Lei Guo | 8 | 172 | 8.74 |
| wenhua | 9 | 337 | 34.19 |