IDEAS home Printed from https://ideas.repec.org/a/gam/jagris/v12y2022i12p2161-d1004543.html
   My bibliography  Save this article

Utility of Deep Learning Algorithms in Initial Flowering Period Prediction Models

Author

Listed:
  • Guanjie Jiao

    (School of Environmental Science and Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China)

  • Xiawei Shentu

    (School of Applied Meteorology, Nanjing University of Information Science and Technology, Nanjing 210044, China)

  • Xiaochen Zhu

    (School of Applied Meteorology, Nanjing University of Information Science and Technology, Nanjing 210044, China)

  • Wenbo Song

    (School of Artificial Intelligence (School of Future Technology), Nanjing University of Information Science and Technology, Nanjing 210044, China)

  • Yujia Song

    (School of Applied Meteorology, Nanjing University of Information Science and Technology, Nanjing 210044, China)

  • Kexuan Yang

    (School of Applied Meteorology, Nanjing University of Information Science and Technology, Nanjing 210044, China)

Abstract

The application of a deep learning algorithm (DL) can more accurately predict the initial flowering period of Platycladus orientalis (L.) Franco. In this research, we applied DL to establish a nationwide long-term prediction model of the initial flowering period of P. orientalis and analyzed the contribution rate of meteorological factors via Shapely Additive Explanation (SHAP). Based on the daily meteorological data of major meteorological stations in China from 1963–2015 and the observation of initial flowering data from 23 phenological stations, we established prediction models by using recurrent neural network (RNN), long short-term memory (LSTM) and gated recurrent unit (GRU). The mean absolute error (MAE), mean absolute percentage error (MAPE), and coefficient of determination ( R 2 ) were used as training effect indicators to evaluate the prediction accuracy. The simulation results show that the three models are applicable to the prediction of the initial flowering of P. orientalis nationwide in China, with the average accuracy of the GRU being the highest, followed by LSTM and the RNN, which is significantly higher than the prediction accuracy of the regression model based on accumulated air temperature. In the interpretability analysis, the factor contribution rates of the three models are similar, the 46 temperature type factors have the highest contribution rate with 58.6% of temperature factors’ contribution rate being higher than 0 and average contribution rate being 5.48 × 10 −4 , and the stability of the contribution rate of the factors related to the daily minimum temperature factor has obvious fluctuations with an average standard deviation of 8.57 × 10 −3 , which might be related to the plants being sensitive to low temperature stress. The GRU model can accurately predict the change rule of the initial flowering, with an average accuracy greater than 98%, and the simulation effect is the best, indicating that the potential application of the GRU model is the prediction of initial flowering.

Suggested Citation

  • Guanjie Jiao & Xiawei Shentu & Xiaochen Zhu & Wenbo Song & Yujia Song & Kexuan Yang, 2022. "Utility of Deep Learning Algorithms in Initial Flowering Period Prediction Models," Agriculture, MDPI, vol. 12(12), pages 1-17, December.
    • Handle: RePEc:gam:jagris:v:12:y:2022:i:12:p:2161-:d:1004543
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2077-0472/12/12/2161/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2077-0472/12/12/2161/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Muhammad Amin & Muhammad Nauman Akram & Qasim Ramzan, 2022. "Bayesian estimation of ridge parameter under different loss functions," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 51(12), pages 4055-4071, May.
    2. Georgeta Bandoc & Adrian Piticar & Cristian Patriche & Bogdan Roșca & Elena Dragomir, 2022. "Climate Warming-Induced Changes in Plant Phenology in the Most Important Agricultural Region of Romania," Sustainability, MDPI, vol. 14(5), pages 1-23, February.
    3. Camille Parmesan & Gary Yohe, 2003. "A globally coherent fingerprint of climate change impacts across natural systems," Nature, Nature, vol. 421(6918), pages 37-42, January.
    4. E. M. Wolkovich & B. I. Cook & J. M. Allen & T. M. Crimmins & J. L. Betancourt & S. E. Travers & S. Pau & J. Regetz & T. J. Davies & N. J. B. Kraft & T. R. Ault & K. Bolmgren & S. J. Mazer & G. J. McC, 2012. "Warming experiments underpredict plant phenological responses to climate change," Nature, Nature, vol. 485(7399), pages 494-497, May.
    5. Terry L. Root & Jeff T. Price & Kimberly R. Hall & Stephen H. Schneider & Cynthia Rosenzweig & J. Alan Pounds, 2003. "Fingerprints of global warming on wild animals and plants," Nature, Nature, vol. 421(6918), pages 57-60, January.
    6. Gian-Reto Walther & Eric Post & Peter Convey & Annette Menzel & Camille Parmesan & Trevor J. C. Beebee & Jean-Marc Fromentin & Ove Hoegh-Guldberg & Franz Bairlein, 2002. "Ecological responses to recent climate change," Nature, Nature, vol. 416(6879), pages 389-395, March.
    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. Xiuguo Zou & Zheng Liu & Xiaochen Zhu & Wentian Zhang & Yan Qian & Yuhua Li, 2023. "Application of Vision Technology and Artificial Intelligence in Smart Farming," Agriculture, MDPI, vol. 13(11), pages 1-4, November.

    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. Wesley R. Brooks & Stephen C. Newbold, 2013. "Ecosystem damages in integrated assessment models of climate change," NCEE Working Paper Series 201302, National Center for Environmental Economics, U.S. Environmental Protection Agency, revised Mar 2013.
    2. Víctor Rincón & Javier Velázquez & Derya Gülçin & Aida López-Sánchez & Carlos Jiménez & Ali Uğur Özcan & Juan Carlos López-Almansa & Tomás Santamaría & Daniel Sánchez-Mata & Kerim Çiçek, 2023. "Mapping Priority Areas for Connectivity of Yellow-Winged Darter ( Sympetrum flaveolum , Linnaeus 1758) under Climate Change," Land, MDPI, vol. 12(2), pages 1-39, January.
    3. Lucie Kuczynski & Mathieu Chevalier & Pascal Laffaille & Marion Legrand & Gaël Grenouillet, 2017. "Indirect effect of temperature on fish population abundances through phenological changes," PLOS ONE, Public Library of Science, vol. 12(4), pages 1-13, April.
    4. Sang-Don Lee, 2017. "Global Warming Leading to Phenological Responses in the Process of Urbanization, South Korea," Sustainability, MDPI, vol. 9(12), pages 1-27, November.
    5. Brooks, Wesley R. & Newbold, Stephen C., 2014. "An updated biodiversity nonuse value function for use in climate change integrated assessment models," Ecological Economics, Elsevier, vol. 105(C), pages 342-349.
    6. Liam D. Bailey & Martijn Pol & Frank Adriaensen & Aneta Arct & Emilio Barba & Paul E. Bellamy & Suzanne Bonamour & Jean-Charles Bouvier & Malcolm D. Burgess & Anne Charmantier & Camillo Cusimano & Bla, 2022. "Bird populations most exposed to climate change are less sensitive to climatic variation," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
    7. Dissanayake, Sahan T.M. & Önal, Hayri & Westervelt, James D. & Balbach, Harold E., 2012. "Incorporating species relocation in reserve design models: An example from Ft. Benning GA," Ecological Modelling, Elsevier, vol. 224(1), pages 65-75.
    8. Jing Zhen & Xinyuan Wang & Qingkai Meng & Jingwei Song & Ying Liao & Bo Xiang & Huadong Guo & Chuansheng Liu & Ruixia Yang & Lei Luo, 2018. "Fine-Scale Evaluation of Giant Panda Habitats and Countermeasures against the Future Impacts of Climate Change and Human Disturbance (2015–2050): A Case Study in Ya’an, China," Sustainability, MDPI, vol. 10(4), pages 1-19, April.
    9. Kato, E., 2009. "Soil and water conservation technologies: a buffer against production risk in the face of climate change?: insights from the Nile Basin in Ethiopia," IWMI Working Papers H042477, International Water Management Institute.
    10. Omann, Ines & Stocker, Andrea & Jäger, Jill, 2009. "Climate change as a threat to biodiversity: An application of the DPSIR approach," Ecological Economics, Elsevier, vol. 69(1), pages 24-31, November.
    11. V. P. Khanduri & C. M. Sharma & S. P. Singh, 2008. "The effects of climate change on plant phenology," Environment Systems and Decisions, Springer, vol. 28(2), pages 143-147, June.
    12. Gregorio Moreno-Rueda & Juan Pleguezuelos & Esmeralda Alaminos, 2009. "Climate warming and activity period extension in the Mediterranean snake Malpolon monspessulanus," Climatic Change, Springer, vol. 92(1), pages 235-242, January.
    13. Karyn Tabor & Jennifer Hewson & Hsin Tien & Mariano González-Roglich & David Hole & John W. Williams, 2018. "Tropical Protected Areas Under Increasing Threats from Climate Change and Deforestation," Land, MDPI, vol. 7(3), pages 1-14, July.
    14. Mayeul Dalleau & Stéphane Ciccione & Jeanne A Mortimer & Julie Garnier & Simon Benhamou & Jérôme Bourjea, 2012. "Nesting Phenology of Marine Turtles: Insights from a Regional Comparative Analysis on Green Turtle (Chelonia mydas)," PLOS ONE, Public Library of Science, vol. 7(10), pages 1-13, October.
    15. Richard Tol, 2011. "Regulating knowledge monopolies: the case of the IPCC," Climatic Change, Springer, vol. 108(4), pages 827-839, October.
    16. Anne Goodenough & Adam Hart, 2013. "Correlates of vulnerability to climate-induced distribution changes in European avifauna: habitat, migration and endemism," Climatic Change, Springer, vol. 118(3), pages 659-669, June.
    17. Fabina, Nicholas S. & Abbott, Karen C. & Gilman, R.Tucker, 2010. "Sensitivity of plant–pollinator–herbivore communities to changes in phenology," Ecological Modelling, Elsevier, vol. 221(3), pages 453-458.
    18. Ye, Qing & Yang, Xiaoguang & Dai, Shuwei & Chen, Guangsheng & Li, Yong & Zhang, Caixia, 2015. "Effects of climate change on suitable rice cropping areas, cropping systems and crop water requirements in southern China," Agricultural Water Management, Elsevier, vol. 159(C), pages 35-44.
    19. John H Matthews & Bart AJ Wickel & Sarah Freeman, 2011. "Converging Currents in Climate-Relevant Conservation: Water, Infrastructure, and Institutions," PLOS Biology, Public Library of Science, vol. 9(9), pages 1-4, September.
    20. Brandt, Laura A. & Benscoter, Allison M. & Harvey, Rebecca & Speroterra, Carolina & Bucklin, David & Romañach, Stephanie S. & Watling, James I. & Mazzotti, Frank J., 2017. "Comparison of climate envelope models developed using expert-selected variables versus statistical selection," Ecological Modelling, Elsevier, vol. 345(C), pages 10-20.

    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:12:y:2022:i:12:p:2161-:d:1004543. 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.