A Lightweight Full-Spectrum Embedding Model for Multi-Property Prediction from Soil Vis-NIR Spectra

Soil visible–near-infrared (Vis-NIR) spectroscopy enables rapid, high-throughput estimation of soil physicochemical properties, but complex deep learning models can increase computational cost and reproducibility burden. This study developed a lightweight full-spectrum embedding multilayer perceptron (LSE-MLP) for simultaneous prediction of soil pH, organic carbon (OC), and total nitrogen (N). LSE-MLP maps 4200-dimensional spectra into a compact, learnable representation and uses residual MLP modules for subsequent nonlinear regression. Using the LUCAS2009 dataset, LSE-MLP was compared with PLSR, SVR-RBF, 1D-CNN, ResNet-1D, and Transformer-1D under a POINT_ID-grouped cross-validation framework. Model performance was assessed using internal split sensitivity analysis, ablation experiments, multi-random-seed analysis, and an independent blind test set. On this test set, LSE-MLP achieved R2 values of 0.9437, 0.9519, and 0.9363 for pH, OC, and N, respectively. Its computational efficiency was primarily reflected in low floating-point operations (FLOPs) and fast batch CPU inference, rather than the smallest parameter count or model storage footprint. Supplementary experiments indicated that band compression reduced input dimensionality with limited performance loss, whereas direct transfer of frozen LUCAS-trained models to KSSL and AUT showed substantial performance degradation associated with spectral and target-distribution differences. Overall, LSE-MLP provides a practical and computationally efficient option for soil property prediction using Vis-NIR spectroscopy under the evaluated conditions, while reliable cross-dataset transfer requires further investigation.