Name
Title | ||
|---|---|---|
A Bayesian framework for joint structure and colour based pixel-wise classification of grapevine proximal images. |
Abstract | ||
|---|---|---|
•Joint structure-colour features based on structure tensors are efficient modelling tools.•Models enable a performant pixel-wise classification of. leaves, fruit, flowers and stems.•A unique framework can be applied to various phenological stages.•Precision and recall rates reach between 85% and 95% depending on and phenological stages.•Models are stable, easy to tune and robust to scale parameters. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1016/j.compag.2019.02.017 | Computers and Electronics in Agriculture |
Keywords | Field | DocType |
Proximal sensing,Grapevine,Texture,Parametric classification,Local structure tensor | Computer vision,Precision and recall,Filter (signal processing),Feature extraction,Precision agriculture,RGB color model,Spatial variability,Artificial intelligence,Pixel,Engineering,Bayesian probability | Journal |
Volume | ISSN | Citations |
158 | 0168-1699 | 1 |
PageRank | References | Authors |
0.36 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| F. Abdelghafour | 1 | 1 | 0.36 |
| Roxana-Gabriela Rosu | 2 | 7 | 1.12 |
| Barna Keresztes | 3 | 3 | 1.88 |
| Christian Germain | 4 | 113 | 18.95 |
| Jean Pierre Da Costa | 5 | 66 | 7.09 |