SoilSound: Smartphone-based Soil Moisture Estimation

• URL: http://arxiv.org/abs/2509.09823v1
• Date: Thu, 11 Sep 2025 19:49:30 GMT
• Title: SoilSound: Smartphone-based Soil Moisture Estimation
• Authors: Yixuan Gao, Tanvir Ahmed, Shuang He, Zhongqi Cheng, Rajalakshmi Nandakumar,
• Abstract summary: Soil moisture monitoring is essential for agriculture and environmental management.<n>Existing methods require either invasive probes disturbing the soil or specialized equipment, limiting access to the public.<n>We present SoilSound, a smartphone-based acoustic sensing system that can measure soil moisture without disturbing the soil.
• Score: 18.512192430760738
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Soil moisture monitoring is essential for agriculture and environmental management, yet existing methods require either invasive probes disturbing the soil or specialized equipment, limiting access to the public. We present SoilSound, an ubiquitous accessible smartphone-based acoustic sensing system that can measure soil moisture without disturbing the soil. We leverage the built-in speaker and microphone to perform a vertical scan mechanism to accurately measure moisture without any calibration. Unlike existing work that use transmissive properties, we propose an alternate model for acoustic reflections in soil based on the surface roughness effect to enable moisture sensing without disturbing the soil. The system works by sending acoustic chirps towards the soil and recording the reflections during a vertical scan, which are then processed and fed to a convolutional neural network for on-device soil moisture estimation with negligible computational, memory, or power overhead. We evaluated the system by training with curated soils in boxes in the lab and testing in the outdoor fields and show that SoilSound achieves a mean absolute error (MAE) of 2.39% across 10 different locations. Overall, the evaluation shows that SoilSound can accurately track soil moisture levels ranging from 15.9% to 34.0% across multiple soil types, environments, and users; without requiring any calibration or disturbing the soil, enabling widespread moisture monitoring for home gardeners, urban farmers, citizen scientists, and agricultural communities in resource-limited settings.

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