Utility of Deep Learning Algorithms in Initial Flowering Period Prediction Models

Guanjie Jiao, Xiawei Shentu, Xiaochen Zhu (), Wenbo Song, Yujia Song and Kexuan Yang
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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

Agriculture, 2022, vol. 12, issue 12, 1-17

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.

Keywords: P. orientalis; recurrent neural network; inverse distance weighting; accumulated air temperature (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: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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