Super Resolution Generative Adversarial Network (SRGANs) for Wheat Stripe Rust Classification Article Swipe
Muhammad Hassan Maqsood
,
Rafia Mumtaz
,
Ihsan Ul Haq
,
Uferah Shafi
,
Syed Mohammad Hassan Zaidi
,
Maryam Hafeez
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.3390/s21237903
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.3390/s21237903
Wheat yellow rust is a common agricultural disease that affects the crop every year across the world. The disease not only negatively impacts the quality of the yield but the quantity as well, which results in adverse impact on economy and food supply. It is highly desired to develop methods for fast and accurate detection of yellow rust in wheat crop; however, high-resolution images are not always available which hinders the ability of trained models in detection tasks. The approach presented in this study harnesses the power of super-resolution generative adversarial networks (SRGAN) for upsampling the images before using them to train deep learning models for the detection of wheat yellow rust. After preprocessing the data for noise removal, SRGANs are used for upsampling the images to increase their resolution which helps convolutional neural network (CNN) in learning high-quality features during training. This study empirically shows that SRGANs can be used effectively to improve the quality of images and produce significantly better results when compared with models trained using low-resolution images. This is evident from the results obtained on upsampled images, i.e., 83% of overall test accuracy, which are substantially better than the overall test accuracy achieved for low-resolution images, i.e., 75%. The proposed approach can be used in other real-world scenarios where images are of low resolution due to the unavailability of high-resolution camera in edge devices.
Related Topics
Concepts
Computer science
Artificial intelligence
Upsampling
Rust (programming language)
Convolutional neural network
Deep learning
Preprocessor
Stripe rust
Pattern recognition (psychology)
Computer vision
Machine learning
Image (mathematics)
Gene
Chemistry
Programming language
Biochemistry
Plant disease resistance
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/s21237903
- https://www.mdpi.com/1424-8220/21/23/7903/pdf?version=1638256717
- OA Status
- gold
- Cited By
- 31
- References
- 25
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3216854021
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3216854021Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/s21237903Digital Object Identifier
- Title
-
Super Resolution Generative Adversarial Network (SRGANs) for Wheat Stripe Rust ClassificationWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-11-26Full publication date if available
- Authors
-
Muhammad Hassan Maqsood, Rafia Mumtaz, Ihsan Ul Haq, Uferah Shafi, Syed Mohammad Hassan Zaidi, Maryam HafeezList of authors in order
- Landing page
-
https://doi.org/10.3390/s21237903Publisher landing page
- PDF URL
-
https://www.mdpi.com/1424-8220/21/23/7903/pdf?version=1638256717Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.mdpi.com/1424-8220/21/23/7903/pdf?version=1638256717Direct OA link when available
- Concepts
-
Computer science, Artificial intelligence, Upsampling, Rust (programming language), Convolutional neural network, Deep learning, Preprocessor, Stripe rust, Pattern recognition (psychology), Computer vision, Machine learning, Image (mathematics), Gene, Chemistry, Programming language, Biochemistry, Plant disease resistanceTop concepts (fields/topics) attached by OpenAlex
- Cited by
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31Total citation count in OpenAlex
- Citations by year (recent)
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2025: 8, 2024: 14, 2023: 5, 2022: 4Per-year citation counts (last 5 years)
- References (count)
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25Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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