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

TL;DR

SoilX is a novel calibration-free soil sensing system that jointly measures soil moisture, nutrients, carbon, and aluminosilicates using contrastive cross-component learning and a specialized antenna array, improving accuracy and generalization.

Contribution

We introduce SoilX, a calibration-free soil sensing system that models key soil components jointly and employs contrastive learning and a new antenna design to eliminate the need for recalibration.

Findings

Reduces estimation errors by up to 31.5% compared to baselines.

Effectively generalizes to unseen fields.

Disentangles cross-component interference with novel learning techniques.

Abstract

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., retraining 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 the 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.