DGPRT-ResNet: A Difference-Guided and Prototype-Regularized Residual Network with Trend Reconstruction for Small-Sample Hyperspectral Soil Total Nitrogen Prediction

Accurate and rapid estimation of soil total nitrogen (TN) from visible–near-infrared hyperspectral data is important for precision agriculture, but small-sample modeling remains challenging because limited labeled samples often lead to unstable feature representations and insufficient use of local spectral variations. This study proposes DGPRT-ResNet, a difference-guided and prototype-regularized residual network with trend reconstruction, for small-sample hyperspectral soil TN prediction. A 2000-sample subset was constructed from the LUCAS 2009 soil spectral library. The spectra were preprocessed using multiplicative scatter correction and first-order derivative transformation, and piecewise pooling averaging was used to obtain 128-dimensional input representations. Based on a one-dimensional residual backbone, the proposed model integrates difference-guided residual calibration to enhance local spectral-shape variations, a prototype-regularized regression head to constrain the deep embedding distribution, and a segmental trend reconstruction auxiliary branch to preserve global spectral trends. Experimental results showed that DGPRT-ResNet achieved an R2 of 0.926 and an RMSE of 0.985 g/kg on the test set, outperforming the baseline ResNet and its ablated variants. These results indicate that combining local difference modeling, feature-space regularization, and trend-preserving auxiliary supervision can improve the prediction accuracy of hyperspectral regression for soil TN estimation under the evaluated limited-sample setting.