Dynamic gating-enhanced deep learning model with multi-source remote sensing synergy for optimizing wheat yield estimation

Dynamic gating-enhanced deep learning model with multi-source remote sensing synergy for optimizing wheat yield estimation

IntroductionAccurate wheat yield estimation is crucial for efficient crop management. This study introduces the Spatio–Temporal Fusion Mixture of Experts (STF-MoE) model, an innovative deep learning framework built upon an LSTM-Transformer architecture.MethodsThe STF-MoE model incorporates a heterog...

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Bibliographic Details
Main Authors: Jian Li, Junrui Kang, Jian Lu, Hongkun Fu, Zheng Li, Baoqi Liu, Xinglei Lin, Jiawei Zhao, Hengxu Guan, He Liu, Zhihan Liu
Format: Article
Language:English
Published: Frontiers Media S.A. 2025-07-01
Series:Frontiers in Plant Science
Subjects:
multi-source remote sensing
deep learning
wheat yield estimation
transformer
MoE module
Online Access:https://www.frontiersin.org/articles/10.3389/fpls.2025.1640806/full
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https://www.frontiersin.org/articles/10.3389/fpls.2025.1640806/full

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