Improving winter wheat plant nitrogen concentration prediction by combining proximal hyperspectral sensing and weather information with machine learning

Abstract

Timely and accurate prediction of nitrogen (N) status in winter wheat is crucial for guiding precision N management. This study aimed to develop an efficient model for predicting winter wheat plant N concentration (PNC) by integrating proximal hyperspectral sensing data with weather information. Hyperspectral data were collected from six field experiments conducted from 2014 to 2023, which were preprocessed using first-order derivative, log-transformation, and continuum removal methods. Effective spectral bands were selected by least absolute shrinkage and selection operator (LASSO), combined with weather information and analyzed using seven machine learning algorithms. The results indicated that first-order derivative-preprocessed bands combined with Elastic Net Regression provided the best PNC prediction (coefficient of determination (R²) = 0.78, root mean square error (RMSE) = 0.28 % and relative prediction deviation (RPD) = 2.15) among the tested methods. Combining proximal hyperspectral sensing and weather information with machine learning algorithms significantly enhanced winter wheat PNC predictions (R² = 0.79–0.85, RMSE = 0.23–0.27 % and RPD = 2.15–2.56) compared with using proximal hyperspectral sensing (R² = 0.34–0.79, RMSE = 0.28–0.48 % and RPD = 1.23–2.15) alone. This approach offers a promising framework for winter wheat PNC prediction to support precision N management. Future work should focus on developing multi-source data fusion strategies, incorporating unmanned aerial vehicle or satellite hyperspectral sensing and machine learning, for large-scale monitoring of crop N status and N management decision making.