Super Resolution Generative Adversarial Network (SRGANs) for Wheat Stripe Rust Classification.

Abstract

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.

Plant phenotyping relevance

小麦の黄さび病を画像から分類する手法において、低解像度画像をSRGANで超解像化し、検出性能を検証しているため、植物病害状態の取得・抽出法が中心的です。

Code and data availability

The paper's wheat yellow rust image dataset (1922 annotated images from NARC fields) is not publicly deposited; the Data Availability Statement says it may be requested from the authors by email. No public code, models, or dataset URLs are provided.

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