Automatic Image-Based Plant Disease Severity Estimation Using Deep Learning
Open Access
- 1 January 2017
- journal article
- research article
- Published by Hindawi Limited in Computational Intelligence and Neuroscience
- Vol. 2017, 1-8
- https://doi.org/10.1155/2017/2917536
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
Funding Information
- Fundamental Research Funds for the Central Universities (2017JC02, TD2014-01)
This publication has 22 references indexed in Scilit:
- Leaf Doctor: A New Portable Application for Quantifying Plant Disease SeverityPlant Disease, 2015
- Image-based phenotyping of plant disease symptomsFrontiers in Plant Science, 2015
- An Automatic Method to Detect and Measure Leaf Disease Symptoms Using Digital Image ProcessingPlant Disease, 2014
- Integrating SOMs and a Bayesian Classifier for Segmenting Diseased Plants in Uncontrolled EnvironmentsThe Scientific World Journal, 2014
- Potential of radial basis function-based support vector regression for apple disease detectionMeasurement, 2014
- Measuring Quantitative Virulence in the Wheat Pathogen Zymoseptoria tritici Using High-Throughput Automated Image AnalysisPhytopathology®, 2014
- Plant Polyphenols, Prenatal Development and Health OutcomesBiological Systems: Open Access, 2014
- Comparison of perceptual color spaces for natural image segmentation tasksOptical Engineering, 2011
- Fast and Accurate Detection and Classification of Plant DiseasesInternational Journal of Computer Applications, 2011
- Plant Disease Severity Estimated Visually, by Digital Photography and Image Analysis, and by Hyperspectral ImagingCritical Reviews in Plant Sciences, 2010