Science-Informed Multitask Transformer for Soil Property Prediction from FTIR Spectroscopy

Monitoring soil health at scale requires tools that are both scientifically robust and computationally efficient. Accurate and efficient estimation of soil chemical properties is essential for sustainable agricultural practices and environmental management. Traditional laboratory-based soil testing methods are labor-intensive, time-consuming, and often impractical for large-scale analysis. Although mid-infrared (MIR) spectroscopy offers a rapid and cost-effective alternative, conventional modeling techniques suffer from inconsistent predictive performance across diverse soil properties. To address this gap, we introduce FTIRNet, a novel multi-task Transformer-based architecture designed to predict multiple soil chemical properties from Fourier-transform infrared (FTIR) spectral data. FTIRNet employs a shared encoder block with task-specific Transformer encoders, enabling the model to learn both general and task-focused spectral representations. These multi-task Transformer encoders incorporate science-informed task relation learning through specialized fusion layers that capture known biogeochemical interdependencies among soil properties. Across a broad set of soil properties, FTIRNet demonstrates consistently superior predictive accuracy compared to traditional approaches.