SoilX: Calibration-Free Comprehensive Soil Sensing through Contrastive Cross-Component Learning

Precision agriculture demands continuous and accurate monitoring of soil moisture M and key macronutrients, including nitrogen N, phosphorus P, and potassium K, to optimize yields and conserve resources. Wireless soil sensing has been explored to measure these four components; however, current solutions require recalibration (i.e., re-train the data processing model) to handle variations in soil texture (characterized by aluminosilicates Al and organic carbon C, limiting their practicality. To address this, we introduce SoilX, a calibration-free soil sensing system that jointly measures six key components: {M, N, P, K, C, Al}. By explicitly modeling C and Al, SoilX eliminates texture- and carbon-dependent recalibration. SoilX incorporates Contrastive Cross-Component Learning (3CL), with two customized terms: the Orthogonality Regularizer and Separation Loss, to effectively disentangle cross-component interference. Additionally, we design a novel Tetrahedral Antenna Array with an antenna-switching mechanism, which can robustly measure soil dielectric permittivity independent of device placement. Extensive experiments demonstrate that SoilX reduces estimation errors by 23.8% to 31.5% over baselines and generalizes well to unseen fields.