Abstract
Yellow rust disease caused by Puccinia striiformis f. sp. tritici, a pathogen in wheat, results in significant losses in wheat production worldwide due to its high destructive property. On the other side, yellow rust can be taken under control by growing resistant cultivars, by the application of fungicides, and by the use of appropriate cultural practices. Thus, it is crucial to detect the disease at an early stage. The current study offers to use computerized models in determining the infection type of yellow rust disease in wheat. Herein, a deep convolutional neural networks-based model, named Yellow-Rust-Xception, was proposed. The model inputs the wheat leaf image and classifies it as no disease, resistant, moderately resistant, moderately susceptible, or susceptible according to the rust severity, i.e. percentage. The convolutional neural networks, a state-of-art approach, have layered structures those inspired by the human brain and able to learn discriminative features from data automatically; thus networks performance match and even surpass humans in task-specific applications, a newly developed dataset containing yellow rust-infected wheat leaf images, was used to train, validate, and test Yellow-Rust-Xception, in result, the test accuracy was 91%. Thus, Yellow-Rust-Xception can be used in determining wheat yellow rust and its severity level.
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Acknowledgements
This study was supported with project 120O960 by The Scientific and Technological Research Council of Turkey (TÜBİTAK). In addition, we would like to thank the Republic of Turkey Ministry of Agriculture and Forestry Directorate of Field Crops Central Research Institute which allowed us to use its resources, Mehmet AYDOĞDU who is a permanent worker there, İsmail KARAKAŞ who is we consulted for photoshoots and used his digital camera and Yozgat Bozok University Boğazlıyan Vocational High School Manager Asst. Prof. Mustafa KOCAKAYA supported us.
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TH, HE, and FV planned the experimental design. TH, FV, and NA collected leaf images. NA and FH completed the image labeling process. TH applied the image preprocessing stages. TH and FV performed the experimental analyses. TH, HE, and FV implemented the proposed approach and validate the results. NA and FH supervised the findings of this work. All authors discussed the results and contributed to the writing of the final manuscript.
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Hayit, T., Erbay, H., Varçın, F. et al. Determination of the severity level of yellow rust disease in wheat by using convolutional neural networks. J Plant Pathol 103, 923–934 (2021). https://doi.org/10.1007/s42161-021-00886-2
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DOI: https://doi.org/10.1007/s42161-021-00886-2