IDEAS home Printed from https://ideas.repec.org/a/gam/jagris/v11y2021i7p607-d584796.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2077-0472/11/7/607/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2077-0472/11/7/607/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    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. Mateusz Ciski & Krzysztof Rząsa & Marek Ogryzek, 2019. "Use of GIS Tools in Sustainable Heritage Management—The Importance of Data Generalization in Spatial Modeling," Sustainability, MDPI, vol. 11(20), pages 1-21, October.
    2. Shouqiang Yin & Jing Li & Jiaxin Liang & Kejing Jia & Zhen Yang & Yuan Wang, 2020. "Optimization of the Weighted Linear Combination Method for Agricultural Land Suitability Evaluation Considering Current Land Use and Regional Differences," Sustainability, MDPI, vol. 12(23), pages 1-25, December.
    3. Chin-Ling Lee & Robert Strong & Kim E. Dooley, 2021. "Analyzing Precision Agriculture Adoption across the Globe: A Systematic Review of Scholarship from 1999–2020," Sustainability, MDPI, vol. 13(18), pages 1-15, September.
    4. Sung Soo Kim & Chong Kyu Lee & Hag Mo Kang & Soo Im Choi & So Hui Jeon & Hyun Kim, 2021. "Land Suitability Evaluation for Wild-Simulated Ginseng Cultivation in South Korea," Land, MDPI, vol. 10(2), pages 1-13, January.
    5. An T. N. Dang & Lalit Kumar & Michael Reid, 2020. "Modelling the Potential Impacts of Climate Change on Rice Cultivation in Mekong Delta, Vietnam," Sustainability, MDPI, vol. 12(22), pages 1-21, November.
    6. S. Abdul Rahaman & S. Aruchamy, 2022. "Land Suitability Evaluation of Tea ( Camellia sinensis L.) Plantation in Kallar Watershed of Nilgiri Bioreserve, India," Geographies, MDPI, vol. 2(4), pages 1-23, November.
    7. Chiranjit Singha & Kishore Chandra Swain & Sanjay Kumar Swain, 2020. "Best Crop Rotation Selection with GIS-AHP Technique Using Soil Nutrient Variability," Agriculture, MDPI, vol. 10(6), pages 1-18, June.
    8. Prapasiri Tongsiri & Wen-Yu Tseng & Yuan Shen & Hung-Yu Lai, 2020. "Comparison of Soil Properties and Organic Components in Infusions According to Different Aerial Appearances of Tea Plantations in Central Taiwan," Sustainability, MDPI, vol. 12(11), pages 1-21, May.
    9. Mehrnoosh Taherizadeh & Arman Niknam & Thong Nguyen-Huy & Gábor Mezősi & Reza Sarli, 2023. "Flash flood-risk areas zoning using integration of decision-making trial and evaluation laboratory, GIS-based analytic network process and satellite-derived information," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 118(3), pages 2309-2335, September.
    10. 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.

    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:11:y:2021:i:7:p:607-:d:584796. 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.