ORIGINAL RESEARCH article

Front. Plant Sci.

Sec. Sustainable and Intelligent Phytoprotection

Volume 16 - 2025 | doi: 10.3389/fpls.2025.1640806

This article is part of the Research TopicStreamlining Digital Agriculture: Advances in Sensing, Processing, and Modeling for Accessible SolutionsView all articles

Dynamic Gating-Enhanced Deep Learning Model with Multi-Source Remote Sensing Synergy for Optimizing Wheat Yield Estimation

Provisionally accepted
Jian  LiJian Li1*Junrui  KangJunrui Kang1Jian  LuJian Lu2Hongkun  FuHongkun Fu3Zheng  LiZheng Li1Baoqi  LiuBaoqi Liu4Xinglei  LinXinglei Lin1Jiawei  ZhaoJiawei Zhao1Hengxu  GuanHengxu Guan1He  LiuHe Liu5*Zhihan  LiuZhihan Liu1
  • 1College of Information Technology, Jilin Agriculture University, Changchun, China
  • 2College of Resources and Environment, Jilin Agriculture University, Changchun, China
  • 3College of Agriculture, Jilin Agriculture University, Changchun, China
  • 4Northeast Institute of Geography and Agroecology Chinese Academy of Sciences, Changchun, China
  • 5College of Engineering and Technology, Jilin Agriculture University, Changchun, China

The final, formatted version of the article will be published soon.

Accurate estimation of wheat yield is the cornerstone of efficient crop management. In this study, the STF-MoE (Spatio-Temporal Fusion Mixture of Experts) model was developed by innovatively introducing a mixture of heterogeneous experts (MoE) mechanism into an LSTM-Transformer framework. This mechanism employs an adaptive gating network to dynamically allocate fused multisource remote sensing features (e.g., near-infrared vegetation reflectance, NIRv; fraction of photosynthetically active radiation absorption, Fpar) to multiple heterogeneous expert networks for refined processing. By integrating outputs from these expert networks, the model efficiently processes these complex features, enabling high-precision yield estimation in six major wheat-producing provinces in China. In the most recent estimation year, the model demonstrated exceptional accuracy (R² = 0.827, RMSE = 547.7 kg/ha) and exhibited robust performance during historical years and extreme climatic events. Relative importance analysis of input variables revealed that relative humidity (RHum) and digital elevation model (DEM) are the most critical factors influencing yield estimation, while higher values of Fpar , NIRv, and leaf area index (LAI) show significant positive correlations with wheat yield. Furthermore, the STF-MoE model reliably identified key phenological stages for yield formation (March to June) and achieved high-precision yield estimation approximately 1-2 months before harvest. Notably, the model operates independently of future weather conditions, as its wheat yield estimation primarily relies on capturing yield-related features embedded in early-to midgrowing season data.

Keywords: multi-source remote sensing, deep learning, Wheat yield estimation, transformer, MOE module

Received: 05 Jun 2025; Accepted: 30 Jun 2025.

Copyright: © 2025 Li, Kang, Lu, Fu, Li, Liu, Lin, Zhao, Guan, Liu and Liu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence:
Jian Li, College of Information Technology, Jilin Agriculture University, Changchun, China
He Liu, College of Engineering and Technology, Jilin Agriculture University, Changchun, China

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