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Automatic Image-Based Plant Disease Severity Estimation Using Deep Learning - 2017

Automatic Image-Based Plant Disease Severity Estimation Using Deep Learning

Research Area:  Machine Learning

Abstract:

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.

Keywords:  

Author(s) Name:  Guan Wang ,Yu Sun and Jianxin Wang

Journal name:  Computational Intelligence and Neuroscience

Conferrence name:  

Publisher name:  Hindawi

DOI:  10.1155/2017/2917536

Volume Information: