An Open-Source Arduino Architecture for Empirical Moisture Calibration of Commercial Soil NPK Sensors

As global populations rise and arable land per capita shrinks, widespread adoption of precision agriculture is unavoidable. This means there needs to be accessible, accurate means of measuring soil N, P2O5, and K2O levels quickly and cheaply. Current off-the-shelf soil probes for measuring these three key macronutrients are notoriously unreliable, especially at an affordable price point: soil moisture content heavily compromises the N, P, and K values they report.

This paper explores the use of an open-source ($142) hardware system built with a cheap 7-in-1 commercially available soil probe connected to an Arduino UNO microcontroller, combined with a correction algorithm to improve the accuracy of sensor readings.

Measurements from the probe across three commercial soil mixes under carefully controlled watering regimes were used to develop and test generalized linear regression models that corrected the readings. The results were highly successful, drastically improving the model predictions by eliminating most interference from soil moisture for each substrate, yielding high-accuracy predictions (R > 0.97 for each nutrient’s initial development data). Furthermore, the corrected system brought the error from over 100% to below 25% for each nutrient when applied to an independent set of holdout soil samples. Future work will upgrade this prototype into a practical tool for farmers by comparing its readings to professional lab tests, training multivariate machine learning models to compensate for native soil matrices, testing how depth and plant roots affect readings, and building a durable, solar-powered outdoor station that sends data wirelessly. Overall, this project offers a simple, affordable way to test soil and collect farm data.