IDEAS home Printed from https://ideas.repec.org/a/gam/jagris/v12y2022i7p897-d843757.html
   My bibliography  Save this article

Potato Late Blight Severity and Epidemic Period Prediction Based on Vis/NIR Spectroscopy

Author

Listed:
  • Bingru Hou

    (College of Mechanical and Electronic Engineering, Northwest A&F University, Xianyang 712100, China)

  • Yaohua Hu

    (College of Optical, Mechanical and Electrical Engineering, Zhejiang A&F University, Hangzhou 311300, China)

  • Peng Zhang

    (College of Mechanical and Electronic Engineering, Northwest A&F University, Xianyang 712100, China)

  • Lixia Hou

    (College of Mechanical and Electronic Engineering, Northwest A&F University, Xianyang 712100, China)

Abstract

Late blight caused by Phytophthora infestans is a destructive disease in potato production, which can lead to crop failure in severe cases. This study combined visible/near-infrared (Vis/NIR) spectroscopy with machine learning (ML) and chemometric methods for rapid detection of potato late blight. The determination of disease severity was accomplished by two methods directly or indirectly based on differences in reflectance. One approach was to utilize ML algorithms to build a model that directly reflects the relationship between disease level and spectral reflectance. Another method was to first use partial least squares to construct a predictive model of internal physicochemical values, such as relative chlorophyll content (SPAD) and peroxidase (POD) activity, and then use an ML model to classify disease levels based on the predicted values. The classification accuracy based on these two methods could reach up to 99 and 95%, respectively. The changes in physicochemical values during the development of disease were further investigated. Regression models for fitting changes in SPAD value and POD activity were developed based on temperature and incubation time, with determination coefficients of 0.961 and 0.997, respectively. The prediction of epidemic period was realized by combining regression and classification models based on physicochemical values with an accuracy of 88.5%. It is demonstrated that rapid non-destructive determination of physicochemical values based on Vis/NIR spectroscopy for potato late blight detection is feasible. Furthermore, it is possible to guide the control of disease throughout the epidemic period.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jagris:v:12:y:2022:i:7:p:897-:d:843757
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2077-0472/12/7/897/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2077-0472/12/7/897/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Xuan Chu & Pu Miao & Kun Zhang & Hongyu Wei & Han Fu & Hongli Liu & Hongzhe Jiang & Zhiyu Ma, 2022. "Green Banana Maturity Classification and Quality Evaluation Using Hyperspectral Imaging," Agriculture, MDPI, vol. 12(4), pages 1-18, April.
    2. 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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    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. Jingmin Shi & Fanhuai Shi & Xixia Huang, 2023. "Prediction of Maturity Date of Leafy Greens Based on Causal Inference and Convolutional Neural Network," Agriculture, MDPI, vol. 13(2), pages 1-16, February.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jagris:v:12:y:2022:i:7:p:897-:d:843757. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.