Calibration of an Arduino-based low-cost capacitive soil moisture sensor for smart agriculture

• Published in: Journal of Hydrology and Hydromechanics 2022-09, Vol.70 (3), p.330-340
• Main Authors: Kulmány, István Mihály, Bede-Fazekas, Ákos, Beslin, Ana, Giczi, Zsolt, Milics, Gábor, Kovács, Barna, Kovács, Márk, Ambrus, Bálint, Bede, László, Vona, Viktória
• Format: Article
• Language: English
• Subjects: Agricultural economics; Agriculture; Calibration; Clay loam; Data acquisition; Data collection; Digital agriculture; Internet of Things; IoT; Loam; Loam soils; Low cost; Low-cost capacitive soil moisture sensor; Moisture content; Moisture effects; Non-linear regression; Polynomials; Precision Agriculture; Repeatability and Reproducibility study; Reproducibility; Sandy loam; Sandy soils; Sensors; Silt loam; Soil; Soil moisture; Soil moisture content; Soil properties; Soil texture; Sustainability; Sustainability management; Texture; Thermo-gravimetric method; Water content; Water management
• ISSN: 1338-4333; 0042-790X; 1338-4333
• DOI: 10.2478/johh-2022-0014

Agriculture faces several challenges to use the available resources in a more environmentally sustainable manner. One of the most significant is to develop sustainable water management. The modern Internet of Things (IoT) techniques with real-time data collection and visualisation can play an important role in monitoring the readily available moisture in the soil. An automated Arduino-based low-cost capacitive soil moisture sensor has been calibrated and developed for data acquisition. A sensor- and soil-specific calibration was performed for the soil moisture sensors (SKU:SEN0193 - DFROBOT, Shanghai, China). A Repeatability and Reproducibility study was conducted by range of mean methods on clay loam, sandy loam and silt loam soil textures. The calibration process was based on the data provided by the capacitive sensors and the continuously and parallelly measured soil moisture content by the thermogravimetric method. It can be stated that the response of the sensors to changes in soil moisture differs from each other, which was also greatly influenced by different soil textures. Therefore, the calibration according to soil texture was required to ensure adequate measurement accuracy. After the calibration, it was found that a polynomial calibration function (R ≥ 0.89) was the most appropriate way for modelling the behaviour of the sensors at different soil textures.