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Nondestructive Identification of Litchi Downy Blight at Different Stages Based on Spectroscopy Analysis

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  • Jun Li

    (College of Engineering, South China Agricultural University, Guangzhou 510642, China
    Guangdong Laboratory for Lingnan Modern Agriculture, Guangzhou 510640, China)

  • Junpeng Wu

    (College of Engineering, South China Agricultural University, Guangzhou 510642, China)

  • Jiaquan Lin

    (College of Engineering, South China Agricultural University, Guangzhou 510642, China)

  • Can Li

    (College of Engineering, South China Agricultural University, Guangzhou 510642, China)

  • Huazhong Lu

    (Guangdong Academy of Agricultural Sciences, Guangzhou 510640, China)

  • Caixia Lin

    (College of Engineering, South China Agricultural University, Guangzhou 510642, China)

Abstract

Litchi downy blight caused by Peronophythora litchii is the most serious disease in litchi production, storage and transportation. Existing disease identification technology has difficulty identifying litchi downy blight sufficiently early, resulting in economic losses. Thus, the use of diffuse reflectance spectroscopy to identify litchi downy blight at different stages of disease, particularly to achieve the early identification of downy blight, is very important. The diffuse reflectance spectral data of litchi fruits inoculated with P. litchii were collected in the wavelength range of 350–1350 nm. According to the duration of inoculation and expert evaluation, they were divided into four categories: healthy, latent, mild and severe. First, the SG smoothing method and derivation method were used to denoise the spectral curves. Then, the wavelength screening methods competitive adaptive reweighted sampling (CARS) and successive projections algorithm (SPA) were compared to verify that the SPA method was more effective. Eleven characteristic wavelengths were selected, accounting for only 1.1% of the original data. Finally, the characteristic wavelengths were tested by six different classification models, and their accuracy was calculated. Among them, the ANN model performed best, with an accuracy of 90.7%. The results showed that diffuse reflectance spectroscopic technology has potential for identifying litchi downy blight at different stages, providing technical support for the subsequent development of related automatic detection devices.

Suggested Citation

  • Jun Li & Junpeng Wu & Jiaquan Lin & Can Li & Huazhong Lu & Caixia Lin, 2022. "Nondestructive Identification of Litchi Downy Blight at Different Stages Based on Spectroscopy Analysis," Agriculture, MDPI, vol. 12(3), pages 1-17, March.
  • Handle: RePEc:gam:jagris:v:12:y:2022:i:3:p:402-:d:770667
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    References listed on IDEAS

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    1. Altman, Edward I. & Marco, Giancarlo & Varetto, Franco, 1994. "Corporate distress diagnosis: Comparisons using linear discriminant analysis and neural networks (the Italian experience)," Journal of Banking & Finance, Elsevier, vol. 18(3), pages 505-529, May.
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    Cited by:

    1. Lu Lu & Wei Liu & Wenbo Yang & Manyu Zhao & Tinghao Jiang, 2022. "Lightweight Corn Seed Disease Identification Method Based on Improved ShuffleNetV2," Agriculture, MDPI, vol. 12(11), pages 1-18, November.
    2. Bingru Hou & Yaohua Hu & Peng Zhang & Lixia Hou, 2022. "Potato Late Blight Severity and Epidemic Period Prediction Based on Vis/NIR Spectroscopy," Agriculture, MDPI, vol. 12(7), pages 1-17, June.

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