Growth-stage deep learning for UAV hyperspectral estimation of leaf nitrogen content in winter wheat

Leaf nitrogen content (LNC) is critical for crop nutrition and precision fertilization. UAV-based hyperspectral sensing enables rapid field-scale nitrogen diagnosis in winter wheat, but stage-dependent drift in the LNC-spectral relationship limits multi-temporal monitoring. To address this limitation, this study proposed a Growth-stage Deep learning model for Leaf Nitrogen Content estimation in winter wheat (GD-LNC). The core innovation of GD-LNC lies in the synergistic integration of explicit growth-stage embedding with a dynamic feature fusion gating mechanism. The growth stage was incorporated as a key parameter in the deep learning-based hyperspectral model for LNC estimation. This strategy improved the generalization ability of multi-temporal LNC estimation during the grain-filling period. This study utilized the datasets from two growing seasons of multi-factor winter wheat experiments. The datasets included UAV-based hyperspectral images and synchronous ground samples. LNC-sensitive bands were selected via stepwise projection algorithm (SPA) and shuffled frog leaping algorithm (SFLA). Subsequently, a multi-temporal dynamic feature extraction and growth-stage embedding branch was constructed. An adaptive fusion mechanism was then employed to dynamically adjust the contribution weights of each feature. These components together enabled robust estimation and mapping of LNC. Results show hyperspectral features strongly responded to LNC, and SFLA-selected bands retained key spectral information, improving estimation accuracy and stability. GD-LNC outperformed PLSR, LSTM and RF, with SFLA-GD-LNC achieving R² of 0.88 and RMSE of 0.20% (training), and R² of 0.85 with RMSE 0.22% (validation), and R² of 0.88 with RMSE 0.21% (test). The model captured spatial and temporal LNC dynamics, providing a generalizable UAV hyperspectral framework for nitrogen monitoring and cross-stage adaptation.