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Article

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

by 1,2,3, 1,2,3, 1,2,3, 1,2,3, 1,2,3, 1,2,3 and 1,2,3,*
1
School of Environment, Northeast Normal University, Changchun 130024, China
2
Department of Environment, Institute of Natural Disaster Research, Northeast Normal University, Changchun 130024, China
3
Key Laboratory for Vegetation Ecology, Ministry of Education, Changchun 130024, China
*
Author to whom correspondence should be addressed.
Academic Editors: Selma Boumerdassi, Eric Renault, Christopher Robin Bryant and Francesco Marinello
Received: 17 May 2021 / Revised: 18 June 2021 / Accepted: 24 June 2021 / Published: 29 June 2021
(This article belongs to the Special Issue Latest Advances for Smart and Sustainable Agriculture)
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. View Full-Text
Keywords: tea tree; frost disaster; machine learning; frost hazard; space distribution tea tree; frost disaster; machine learning; frost hazard; space distribution
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MDPI and ACS Style

Xu, J.; Guga, S.; Rong, G.; Riao, D.; Liu, X.; Li, K.; Zhang, J. Estimation of Frost Hazard for Tea Tree in Zhejiang Province Based on Machine Learning. Agriculture 2021, 11, 607. https://0-doi-org.brum.beds.ac.uk/10.3390/agriculture11070607

AMA Style

Xu J, Guga S, Rong G, Riao D, Liu X, Li K, Zhang J. Estimation of Frost Hazard for Tea Tree in Zhejiang Province Based on Machine Learning. Agriculture. 2021; 11(7):607. https://0-doi-org.brum.beds.ac.uk/10.3390/agriculture11070607

Chicago/Turabian Style

Xu, Jie, Suri Guga, Guangzhi Rong, Dao Riao, Xingpeng Liu, Kaiwei Li, and Jiquan Zhang. 2021. "Estimation of Frost Hazard for Tea Tree in Zhejiang Province Based on Machine Learning" Agriculture 11, no. 7: 607. https://0-doi-org.brum.beds.ac.uk/10.3390/agriculture11070607

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