无需校准的土壤六成分智能感知系统，可精准监测水分与养分。
SoilX: Calibration-Free Comprehensive Soil Sensing through Contrastive Cross-Component Learning
- 通过对比跨组分学习，解耦土壤中各成分干扰。
- 误差比基线降低23.8%至31.5%，在未见地块上仍表现稳定。
- 适合智慧农业、土壤监测场景，免去频繁校准麻烦。
精准农业需要持续、准确地监测土壤水分（M）及氮（N）、磷（P）、钾（K）等关键大量元素，以优化产量并节约资源。无线土壤传感技术已用于测量这四项指标，但现有方案需针对土壤质地（由铝硅酸盐Al和有机碳C决定）变化重新校准（即重训练数据处理模型），限制了实际应用。为此，我们提出SoilX，一种免校准的土壤感知系统，可同步测量六项关键成分：{M, N, P, K, C, Al}。通过显式建模C和Al，SoilX消除了对质地和碳含量的依赖性校准需求。系统引入对比跨组分学习（3CL），包含正交正则化与分离损失两项定制项，有效解耦多成分间的干扰。此外，设计新型四面体天线阵列与天线切换机制，实现不受设备摆放影响的土壤介电常数稳健测量。大量实验表明，SoilX相比基线方法将估计误差降低23.8%至31.5%，且在未见过的田块上具有良好泛化能力。
原文摘要 · Abstract (English)
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.
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