Super-Resolution of Plant Disease Images for the Acceleration of Image-based Phenotyping and Vigor Diagnosis in Agriculture
Abstract
:1. Introduction
2. Materials and Methods
2.1. Crop Disease Image Dataset
2.2. Performance Evaluation of Super-Resolution
2.3. Performance Evaluation of Disease Classification
2.4. Architecture of SRCNN
2.5. Architecture of CNN for Disease Classification
2.6. Implementation
3. Results
3.1. Super-Resolution
3.2. Disease Classification
4. Discussion
5. Conclusions
Supplementary Materials
Acknowledgments
Author Contributions
Conflicts of Interest
Abbreviations
SRCNN | Super resolution convolutional neural network |
CNN | Convolutional neural network |
MSE | Mean squared error |
PSNR | Peak signal-to-noise ration |
SSIM | Structural similarity |
Appendix A
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ID | Disease | Num. |
---|---|---|
0 | Xanthomonas campestris pv. Vesicatoria | 2127 |
1 | Alternaria solani | 2579 |
2 | Phytophthora infestans | 1910 |
3 | Septoria lycopersici | 1771 |
4 | Tetranychus urticae | 1676 |
5 | Tomato mosaic virus | 373 |
6 | Fulvia fulva | 952 |
7 | Corynespora cassiicola | 1404 |
8 | Tomato yellow leaf curl virus | 5357 |
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Yamamoto, K.; Togami, T.; Yamaguchi, N. Super-Resolution of Plant Disease Images for the Acceleration of Image-based Phenotyping and Vigor Diagnosis in Agriculture. Sensors 2017, 17, 2557. https://doi.org/10.3390/s17112557
Yamamoto K, Togami T, Yamaguchi N. Super-Resolution of Plant Disease Images for the Acceleration of Image-based Phenotyping and Vigor Diagnosis in Agriculture. Sensors. 2017; 17(11):2557. https://doi.org/10.3390/s17112557
Chicago/Turabian StyleYamamoto, Kyosuke, Takashi Togami, and Norio Yamaguchi. 2017. "Super-Resolution of Plant Disease Images for the Acceleration of Image-based Phenotyping and Vigor Diagnosis in Agriculture" Sensors 17, no. 11: 2557. https://doi.org/10.3390/s17112557
APA StyleYamamoto, K., Togami, T., & Yamaguchi, N. (2017). Super-Resolution of Plant Disease Images for the Acceleration of Image-based Phenotyping and Vigor Diagnosis in Agriculture. Sensors, 17(11), 2557. https://doi.org/10.3390/s17112557