SoilX: Calibration-Free Comprehensive Soil Sensing through Contrastive Cross-Component Learning
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arXiv
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| Format: | Preprint |
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2025
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| author | Yang, Kang Yang, Yuanlin Chen, Yuning Yang, Sikai Zhang, Xinyu Du, Wan |
| author_facet | Yang, Kang Yang, Yuanlin Chen, Yuning Yang, Sikai Zhang, Xinyu Du, Wan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_05482 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | SoilX: Calibration-Free Comprehensive Soil Sensing through Contrastive Cross-Component Learning Yang, Kang Yang, Yuanlin Chen, Yuning Yang, Sikai Zhang, Xinyu Du, Wan Machine 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., 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. |
| title | SoilX: Calibration-Free Comprehensive Soil Sensing through Contrastive Cross-Component Learning |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2511.05482 |