Soil knowledge-guided multi-task transformer model for predicting soil properties and crop traits with early warning alerts

Soil properties are vital for soil health, fertility, and crop productivity, yet traditional methods like the Partial Least Squares Regression (PLSR) predict individual properties without capturing their interactions or impacts on crop growth. This study presents the Soil Knowledge-Guided Multi-task Transformer (KGMT) model, which integrates deep learning and multi-task learning to simultaneously predict multiple soil properties and crop traits using UAV hyperspectral imagery, eliminating complex manual feature extraction. By embedding soil knowledge, KGMT improves prediction accuracy and generalization over PLSR and baseline multi-task models. Data from two agricultural plots in Bayannur City, Inner Mongolia, China, demonstrated KGMT's superior performance in predicting six soil properties (alkalinity, salinity, organic matter, potassium, sodium, calcium) and two crop traits (leaf area index, aboveground biomass), achieving up to 20% lower normalized root mean square error. For instance, KGMT yielded higher R 2 values for alkalinity (0.57) and organic matter (0.57) compared to PLSR (0.46 and 0.40, respectively). The model also enhanced cross-region generalization. Additionally, a soil early warning framework was developed, linking pre-planting soil conditions with crop traits to detect potential growth limitations early, enabling proactive management. This approach integrates spectral data with domain knowledge, offering a robust solution for soil and crop management. It contributes to precision agriculture by optimizing soil conditions and promoting sustainable land use practices, highlighting the value of advanced deep learning in agricultural applications. • Innovative Model for Multi-task Prediction : The Soil Knowledge-Guided Multi-task Transformer (KGMT) combines deep learning and domain knowledge to simultaneously predict multiple soil properties and crop growth traits directly from UAV hyperspectral imagery, eliminating the need for manual feature extraction. • Enhanced Prediction Accuracy and Generalization : KGMT outperformed traditional PLSR and baseline multi-task models in predicting six soil properties (e.g., alkalinity, salinity, organic matter, potassium, sodium, and calcium) and crop traits such as leaf area index (R 2 = 0.75) and aboveground biomass (R 2 = 0.82), achieving up to a 20% reduction in normalized root mean square error (nRMSE). • Proactive Soil and Crop Management : A novel soil early warning approach integrates soil properties from the bare soil stage with crop traits during growth, enabling early detection of potential limitations to crop growth and supporting precision agriculture for sustainable land use.