t. Figure 1 Workflow of this study. Figure 2 is based on the dataset used in this research, which includes the three categories of healthy wheat, leaf rust wheat, and stem rust wheat, with image data from farm sites in Ethiopia and Tanzania, and from public images on Google Maps. The dataset can be obtain in the following link: https://zindi.africa/competitions/iclr-workshop-challenge-1-cgiar-computer-vision-for-crop-disease/data (accessed on 16 July 2022). Leaf rust occurs mainly on the leaf area, but it can also arise on the stem, and stem rust occurs mostly on the stem, but it may also appear on the leaf area. Therefore, it is important to judge not only the location of the disease but al
Open resource ↗Zindi · iclr-workshop-challenge-1-cgiar-computer-vision-for-crop-disease · lines:29-38Unverified paper record
Image Classification of Wheat Rust Based on Ensemble Learning.
Sensors (Basel, Switzerland) · 12 Aug 2022 · 10.3390/s22166047
Abstract
Rust is a common disease in wheat that significantly impacts its growth and yield. Stem rust and leaf rust of wheat are difficult to distinguish, and manual detection is time-consuming. With the aim of improving this situation, this study proposes a method for identifying wheat rust based on ensemble learning (WR-EL). The WR-EL method extracts and integrates multiple convolutional neural network (CNN) models, namely VGG, ResNet 101, ResNet 152, DenseNet 169, and DenseNet 201, based on bagging, snapshot ensembling, and the stochastic gradient descent with warm restarts (SGDR) algorithm. The identification results of the WR-EL method were compared to those of five individual CNN models. The results show that the identification accuracy increases by 32%, 19%, 15%, 11%, and 8%. Additionally, we proposed the SGDR-S algorithm, which improved the f1 scores of healthy wheat, stem rust wheat and leaf rust wheat by 2%, 3% and 2% compared to the SGDR algorithm, respectively. This method can more accurately identify wheat rust disease and can be implemented as a timely prevention and control measure, which can not only prevent economic losses caused by the disease, but also improve the yield and quality of wheat.
Plant phenotyping relevance
小麦葉・茎さび病という植物の病徴状態を画像から分類する手法を開発・比較しており、病害フェノタイピング手法が中心である。
abstractthis study proposes a method for identifying wheat rust based on ensemble learning (WR-EL).
abstractThe identification results of the WR-EL method were compared to those of five individual CNN models.
Code and data availability
The paper's wheat rust classification uses the public ICLR Workshop/CGIAR crop disease image dataset (healthy, stem rust, leaf rust wheat images from Ethiopia and Tanzania), which the authors explicitly state is downloadable from the Zindi competition page. No author code or trained models are stated as available (Data
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