Name
Abstract | ||
|---|---|---|
Plant is closely related to humans. How to quickly recognize an unknown plant without related professional knowledge is a huge challenge. With the development of image processing and pattern recognition, it is available for plant recognition based on the technique of image processing. Pulse-coupled neural network is a powerful tool for image processing. It is widely applied in the field of image segmentation, image fusion, feature extraction, etc. Support vector machine is an excellent classifier, which can finish the complex task of data exploration. Based on these two techniques, a novel plant recognition method is proposed in this paper. The key feature is the entropy sequence obtained by pulse-coupled neural network. Other ancillary features can be computed directly by mathematical and morphological methods. Both key feature and ancillary features are employed to represent the unique feature of one plant. Support vector machine in our method is taken as the classifier, which can implement the multi-class classification. Experimental results show that the proposed method can finish the task of plant recognition effectively. Compared with the existing methods, our proposed method has better recognition rate. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1007/s00521-015-1904-1 | Neural Computing and Applications |
Keywords | Field | DocType |
Feature extraction, Image processing, Plant recognition, PCNN | Leaf recognition,Image processing,Feature extraction,Artificial intelligence,Mathematics,Machine learning,Professional knowledge | Journal |
Volume | Issue | ISSN |
27 | 4 | 1433-3058 |
Citations | PageRank | References |
6 | 0.44 | 13 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhaobin Wang | 1 | 170 | 10.17 |
| Xiaoguang Sun | 2 | 15 | 1.94 |
| Yaonan Zhang | 3 | 37 | 9.76 |
| Ying Zhu | 4 | 56 | 13.42 |
| Yide Ma | 5 | 459 | 34.74 |