Research on grain yield prediction model based on wavelet transform and LSTM

Chunhua Zhu; Pengle Li

doi:10.1117/12.2667499

22 February 2023 Research on grain yield prediction model based on wavelet transform and LSTM

Chunhua Zhu, Pengle Li

Proceedings Volume 12587, Third International Seminar on Artificial Intelligence, Networking, and Information Technology (AINIT 2022); 125870O (2023) https://doi.org/10.1117/12.2667499
Event: Third International Seminar on Artificial Intelligence, Networking, and Information Technology (AINIT 2022), 2022, Shanghai, China

Abstract

To improve the accuracy of grain yield prediction, a grain yield prediction model based on wavelet transform and long short-term memory (LSTM) is proposed. Firstly, the original data is decomposed by wavelet transform algorithm to obtain a series of sub-sequences of different scales, and then LSTM prediction models are built for the sub-sequences, finally wavelet reconstruction is used to obtain the predicted yield and analyze the model performance. The article uses China's 1999-2018 grain yield as experimental data. The experiment shows that the method proposed in this article has excellent performance in both short-term and medium-term predictions compared to the existing methods.

Citation Download Citation

Chunhua Zhu and Pengle Li "Research on grain yield prediction model based on wavelet transform and LSTM", Proc. SPIE 12587, Third International Seminar on Artificial Intelligence, Networking, and Information Technology (AINIT 2022), 125870O (22 February 2023); https://doi.org/10.1117/12.2667499

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