Automatic Image-Based Plant Disease Severity Estimation Using Deep Learning
Joint Authors
Sun, Yu
Wang, Jianxin
Wang, Guan
Source
Computational Intelligence and Neuroscience
Issue
Vol. 2017, Issue 2017 (31 Dec. 2017), pp.1-8, 8 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2017-07-05
Country of Publication
Egypt
No. of Pages
8
Main Subjects
Abstract EN
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 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.
American Psychological Association (APA)
Wang, Guan& Sun, Yu& Wang, Jianxin. 2017. Automatic Image-Based Plant Disease Severity Estimation Using Deep Learning. Computational Intelligence and Neuroscience،Vol. 2017, no. 2017, pp.1-8.
https://search.emarefa.net/detail/BIM-1140872
Modern Language Association (MLA)
Wang, Guan…[et al.]. Automatic Image-Based Plant Disease Severity Estimation Using Deep Learning. Computational Intelligence and Neuroscience No. 2017 (2017), pp.1-8.
https://search.emarefa.net/detail/BIM-1140872
American Medical Association (AMA)
Wang, Guan& Sun, Yu& Wang, Jianxin. Automatic Image-Based Plant Disease Severity Estimation Using Deep Learning. Computational Intelligence and Neuroscience. 2017. Vol. 2017, no. 2017, pp.1-8.
https://search.emarefa.net/detail/BIM-1140872
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1140872