Data Acquisition and Analysis for Improving the Utility of Low Cost Soil Moisture Sensors

To cultivate healthy plants and high crop yields, growers must be able to measure soil moisture and irrigate accordingly. Errors in soil moisture measurements can lead to irrigation mismanagement with costly consequences. In this paper, we present a new approach to smart computing for irrigation management to address these challenges at a lower cost. We calibrate low cost, low precision soil moisture sensors to more accurately distinguish wet from dry soils using high cost, high precision Davis Instrument sensors. We investigate different modeling techniques including the natural log of the odds ratio (Log-odds), Monte Carlo simulation, and linear regression to distinguish between wet and moist soils and to establish a trustworthy threshold between these two moisture states. We have also developed a new smartphone application that simplifies the process of data collection and implements our analysis approach. The application is extensible by others and provides growers with low cost, data-driven decision support for irrigation. We implement our approach for UCSB’s Edible Campus student farm and empirically evaluate it using multiple test beds. Our results show an accuracy rate of 91% and lowers costs by 4x per deployment, making it useful for gardeners and farmers alike.