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Estimation of Frost Hazard for Tea Tree in Zhejiang Province Based on Machine Learning

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  • Jie Xu

    (School of Environment, Northeast Normal University, Changchun 130024, China
    Department of Environment, Institute of Natural Disaster Research, Northeast Normal University, Changchun 130024, China
    Key Laboratory for Vegetation Ecology, Ministry of Education, Changchun 130024, China)

  • Suri Guga

    (School of Environment, Northeast Normal University, Changchun 130024, China
    Department of Environment, Institute of Natural Disaster Research, Northeast Normal University, Changchun 130024, China
    Key Laboratory for Vegetation Ecology, Ministry of Education, Changchun 130024, China)

  • Guangzhi Rong

    (School of Environment, Northeast Normal University, Changchun 130024, China
    Department of Environment, Institute of Natural Disaster Research, Northeast Normal University, Changchun 130024, China
    Key Laboratory for Vegetation Ecology, Ministry of Education, Changchun 130024, China)

  • Dao Riao

    (School of Environment, Northeast Normal University, Changchun 130024, China
    Department of Environment, Institute of Natural Disaster Research, Northeast Normal University, Changchun 130024, China
    Key Laboratory for Vegetation Ecology, Ministry of Education, Changchun 130024, China)

  • Xingpeng Liu

    (School of Environment, Northeast Normal University, Changchun 130024, China
    Department of Environment, Institute of Natural Disaster Research, Northeast Normal University, Changchun 130024, China
    Key Laboratory for Vegetation Ecology, Ministry of Education, Changchun 130024, China)

  • Kaiwei Li

    (School of Environment, Northeast Normal University, Changchun 130024, China
    Department of Environment, Institute of Natural Disaster Research, Northeast Normal University, Changchun 130024, China
    Key Laboratory for Vegetation Ecology, Ministry of Education, Changchun 130024, China)

  • Jiquan Zhang

    (School of Environment, Northeast Normal University, Changchun 130024, China
    Department of Environment, Institute of Natural Disaster Research, Northeast Normal University, Changchun 130024, China
    Key Laboratory for Vegetation Ecology, Ministry of Education, Changchun 130024, China)

Abstract

Tea trees are the main economic crop in Zhejiang Province. However, spring cold is a frequent occurrence there, causing frost damage to the valuable tea buds. To address this, a regional frost-hazard early-warning system is needed. In this study, frost damage area was estimated based on topography and meteorology, as well as longitude and latitude. Based on support vector machine (SVM) and artificial neural networks (ANNs), a multi-class classification model was proposed to estimate occurrence of regional frost disasters using tea frost cases from 2017. Results of the two models were compared, and optimal parameters were adjusted through multiple iterations. The highest accuracies of the two models were 83.8% and 75%, average accuracies were 79.3% and 71.3%, and Kappa coefficients were 79.1% and 67.37%. The SVM model was selected to establish spatial distribution of spring frost damage to tea trees in Zhejiang Province in 2016. Pearson’s correlation coefficient between prediction results and meteorological yield was 0.79 ( p < 0.01), indicating consistency. Finally, the importance of model factors was assessed using sensitivity analysis. Results show that relative humidity and wind speed are key factors influencing accuracy of predictions. This study supports decision-making for hazard prediction and defense for tea trees facing frost.

Suggested Citation

  • Jie Xu & Suri Guga & Guangzhi Rong & Dao Riao & Xingpeng Liu & Kaiwei Li & Jiquan Zhang, 2021. "Estimation of Frost Hazard for Tea Tree in Zhejiang Province Based on Machine Learning," Agriculture, MDPI, vol. 11(7), pages 1-16, June.
    • Handle: RePEc:gam:jagris:v:11:y:2021:i:7:p:607-:d:584796
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    References listed on IDEAS

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    1. Sadeeka Layomi Jayasinghe & Lalit Kumar & Janaki Sandamali, 2019. "Assessment of Potential Land Suitability for Tea ( Camellia sinensis (L.) O. Kuntze) in Sri Lanka Using a GIS-Based Multi-Criteria Approach," Agriculture, MDPI, vol. 9(7), pages 1-25, July.
    2. Yaojie Yue & Yao Zhou & Jing’ai Wang & Xinyue Ye, 2016. "Assessing Wheat Frost Risk with the Support of GIS: An Approach Coupling a Growing Season Meteorological Index and a Hybrid Fuzzy Neural Network Model," Sustainability, MDPI, vol. 8(12), pages 1-21, December.
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    Cited by:

    1. Juliet Chebet Moso & Stéphane Cormier & Cyril de Runz & Hacène Fouchal & John Mwangi Wandeto, 2021. "Anomaly Detection on Data Streams for Smart Agriculture," Agriculture, MDPI, vol. 11(11), pages 1-17, November.
    2. Ying Han & Yongjian He & Zhuoran Liang & Guoping Shi & Xiaochen Zhu & Xinfa Qiu, 2023. "Risk Assessment and Application of Tea Frost Hazard in Hangzhou City Based on the Random Forest Algorithm," Agriculture, MDPI, vol. 13(2), pages 1-14, January.

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