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

ABSTRACT:

Introduction

Accurate 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.

Methods

The STF-MoE model incorporates a heterogeneous Mixture of Experts (MoE) mechanism with an adaptive gating network. This design dynamically processes fused multi-source remote sensing features (e.g., near-infrared vegetation reflectance, NIRv; fraction of photosynthetically active radiation absorption, Fpar) and environmental variables (e.g., relative humidity, digital elevation model) across multiple expert networks. The model was applied to estimate wheat yield in six major Chinese provinces.

Results

The STF-

SUBMITTER: Li J 

PROVIDER: S-EPMC12318938 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature