Name
Abstract | ||
|---|---|---|
Currently, various agricultural image classification tasks are carried out on high-resolution images. However, in some cases, we cannot get enough high-resolution images for classification, which significantly affects classification performance. In this paper, we design a crop disease classification network based on Enhanced Super-Resolution Generative adversarial networks (ESRGAN) when only an insufficient number of low-resolution target images are available. First, ESRGAN is used to recover super-resolution crop images from low-resolution images. Transfer learning is applied in model training to compensate for the lack of training samples. Then, we test the performance of the generated super-resolution images in crop disease classification task. Extensive experiments show that using the fine-tuned ESRGAN model can recover realistic crop information and improve the accuracy of crop disease classification, compared with the other four image super-resolution methods. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.3390/s20164601 | SENSORS |
Keywords | DocType | Volume |
super-resolution,Generative Adversarial Networks,Convolutional Neural Networks,disease classification | Journal | 20 |
Issue | ISSN | Citations |
16 | 1424-8220 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Juan Wen | 1 | 0 | 1.69 |
| Yangjing Shi | 2 | 0 | 0.34 |
| Xiaoshi Zhou | 3 | 0 | 0.34 |
| Yiming Xue | 4 | 17 | 6.28 |