Journal of Agricultural Science and Technology ›› 2023, Vol. 25 ›› Issue (8): 126-137.DOI: 10.13304/j.nykjdb.2022.0866


Development and Verification of Prediction Model for Grape Downy Mildew Based on Machine Learning

Fengxia BIAN1(), Kaige LIU2, Xinmin RONG1()   

  1. 1.Shihezi Academy of Agricultural Sciences,Xinjiang Shihezi 832000,China
    2.Agricultural College,Shihezi University,Xinjiang Shihezi 832003,China
  • Received:2022-10-12 Accepted:2022-12-29 Online:2023-08-20 Published:2023-09-07
  • Contact: Xinmin RONG


边凤霞1(), 刘凯歌2, 容新民1()   

  1. 1.石河子农业科学研究院,新疆 石河子 832000
    2.石河子大学农学院,新疆 石河子 832003
  • 通讯作者: 容新民
  • 作者简介:边凤霞
  • 基金资助:


Grape downy mildew is one of the major diseases on grapes, which can occur from seedling to fruit maturity, often causing destructive losses in rainy years. To accurately predict the occurrence of grape downy mildew and minimize the hazards of grape downy mildew, based on the agrometeorological data and disease occurrence data during grape growth in 2020, a prediction model for the occurrence of grape downy mildew was developed by 4 machine learning algorithms (binary logistic regression, support vector machine, decision tree and K-nearest neighbors), and the models were verified using the data in 2021. The results showed that the decision tree model had the best evaluation indicators in the disease prediction model, which the accuracy was 94%, and the precision, recall and F1-score was 91%, 90% and 91%, respectively. The decision tree model’s performance was still better than the other models using validation data. Therefore, the decision tree model could be further used to develop early warning systems for grape downy mildew, which should provide technical support and decision-making guidance for controlling it in production.

Key words: machine learning, grape, downy mildew, prediction model



关键词: 机器学习, 葡萄, 霜霉病, 预测模型

CLC Number: