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

Jie Xu, Suri Guga, Guangzhi Rong, Dao Riao, Xingpeng Liu, Kaiwei Li and Jiquan Zhang
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Jie Xu: School of Environment, Northeast Normal University, Changchun 130024, China
Suri Guga: School of Environment, Northeast Normal University, Changchun 130024, China
Guangzhi Rong: School of Environment, Northeast Normal University, Changchun 130024, China
Dao Riao: School of Environment, Northeast Normal University, Changchun 130024, China
Xingpeng Liu: School of Environment, Northeast Normal University, Changchun 130024, China
Kaiwei Li: School of Environment, Northeast Normal University, Changchun 130024, China
Jiquan Zhang: School of Environment, Northeast Normal University, Changchun 130024, China

Agriculture, 2021, vol. 11, issue 7, 1-16

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.

Keywords: tea tree; frost disaster; machine learning; frost hazard; space distribution (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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