Name
Abstract | ||
|---|---|---|
AbstractAutomatic and accurate estimation of disease severity is essential for food security, disease management, and yield loss prediction. Deep learning, the latest breakthrough in computer vision, is promising for fine-grained disease severity classification, as the method avoids the labor-intensive feature engineering and threshold-based segmentation. Using the apple black rot images in the PlantVillage dataset, which are further annotated by botanists with four severity stages as ground truth, a series of deep convolutional neural networks are trained to diagnose the severity of the disease. The performances of shallow networks trained from scratch and deep models fine-tuned by transfer learning are evaluated systemically in this paper. The best model is the deep VGG16 model trained with transfer learning, which yields an overall accuracy of 90.4% on the hold-out test set. The proposed deep learning model may have great potential in disease control for modern agriculture. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1155/2017/2917536 | Periodicals |
Field | DocType | Volume |
Disease,Pattern recognition,Segmentation,Convolutional neural network,Computer science,Transfer of learning,Feature engineering,Ground truth,Artificial intelligence,Deep learning,Machine learning,Test set | Journal | 2017 |
Issue | ISSN | Citations |
1 | 1687-5265 | 12 |
PageRank | References | Authors |
0.77 | 6 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Guan Wang | 1 | 21 | 2.44 |
| Yu Sun | 2 | 22 | 3.15 |
| Jianxin Wang | 3 | 13 | 1.14 |