Name
Abstract | ||
|---|---|---|
Plant image identification has become an interdisciplinary focus in both botanical taxonomy and computer vision. The first plant image dataset collected by mobile phone in natural scene is presented, which contains 10,000 images of 100 ornamental plant species in Beijing Forestry University campus. A 26-layer deep learning model consisting of 8 residual building blocks is designed for large-scale plant classification in natural environment. The proposed model achieves a recognition rate of 91.78% on the BJFU100 dataset, demonstrating that deep learning is a promising technology for smart forestry. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1155/2017/7361042 | COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE |
Field | DocType | Volume |
Image identification,Plant taxonomy,Computer science,Artificial intelligence,Deep learning,Mobile phone,Beijing,Machine learning,Plant identification | Journal | 2017 |
ISSN | Citations | PageRank |
1687-5265 | 6 | 0.54 |
References | Authors | |
4 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yu Sun | 1 | 22 | 3.15 |
| Yuan Liu | 2 | 113 | 32.27 |
| Guan Wang | 3 | 21 | 2.44 |
| Haiyan Zhang | 4 | 6 | 1.21 |