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Assessment of rice leaf blast severity using hyperspectral imaging during late vegetative growth

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Abstract

Rice blast (Magnaporthe grisea) is an epidemic rice disease that reduces rice yield and quality worldwide. The objective of this study was to present and evaluate a data reconstruction method for assessment of rice leaf blast severity using hyperspectral imaging technology at the late vegetative growth stage. Experiments were carried out on Mongolian rice, which is susceptible to the disease. To carry out the study under natural conditions, rice was cultivated without any disease control measures. We obtained hyperspectral images of rice leaves and extracted average spectral reflectance data for entire leaves and undiseased leaf regions. To analyze the hyperspectral data, we presented a spectral reflectance ratio (SRR) data reconstruction method. A support vector machine model was constructed to identify five infection severities based on the transformed data. The classification accuracy of the model at jointing, booting and heading stages was 83.33%, 97.06% and 83.87%, respectively. According to our results, the SRR data reconstruction method presented here can be used to assess rice leaf blast severity during late vegetative growth.

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Acknowledgments

We thank the 503 Laboratory of the College of Information and Electrical Engineering, ShenYang Agricultural University, and the Liaoning Academy of Agricultural Sciences for their support while conducting experiments.

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Authors and Affiliations

  1. ShenYang Agricultural University, Shenyang, 110866, China

    GuoSheng Zhang, TongYu Xu & YouWen Tian

  2. Liaoning Academy of Agricultural Sciences, Shenyang, 110161, China

    Han Xu

  3. Zhejiang Academy of Agricultural Sciences, Hangzhou, 310021, China

    JiaYu Song

  4. South China Agricultural University, Guangzhou, 510642, China

    Yubin Lan

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  1. GuoSheng Zhang
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  2. TongYu Xu
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  4. Han Xu
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Correspondence to GuoSheng Zhang.

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Zhang, G., Xu, T., Tian, Y. et al. Assessment of rice leaf blast severity using hyperspectral imaging during late vegetative growth. Australasian Plant Pathol. 49, 571–578 (2020). https://doi.org/10.1007/s13313-020-00736-2

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  • DOI: https://doi.org/10.1007/s13313-020-00736-2

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