期刊
COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE
卷 2017, 期 -, 页码 -出版社
HINDAWI LTD
DOI: 10.1155/2017/2917536
关键词
-
资金
- Fundamental Research Funds for the Central Universities [2017JC02, TD2014-01]
Automatic 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 VGG16model 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.
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