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 thermo-gravimetric 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 (R2 ≥ 0.89) was the most appropriate way for modelling the behaviour of the sensors at different soil textures.
A soil moisture content map is important for providing information about the distribution of moisture in a given area. Moisture content directly influences agricultural yield thus it is crucial to have accurate and reliable information about moisture distribution and content in the field. Since soil is a porous medium modified generalized Archie’s equation provides the basic formula to calculate moisture content data based on measured ECa. In this study we aimed to find a more accurate and cost effective method for measuring moisture content than manual field sampling. Locations of 25 sampling points were chosen from our research field as a reference. We assumed that soil moisture content could be calculated by measuring apparent electrical conductivity (ECa) using the Veris-3100 on-the-go soil mapping tool. Statistical analysis was carried out on the 10.791 ECa raw data in order to filter the outliers. The applied statistical method was ±1.5 interquartile (IRQ) distance approach. The visualization of soil moisture distribution within the experimental field was carried out by means of ArcGIS/ArcMAP using the inverse distance weighting interpolation method. In the investigated 25 sampling points, coefficient of determination between calculated volumetric moisture content data and measured ECa was R2 = 0.87. According to our results, volumetric moisture content can be mapped by applying ECa measurements in these particular soil types.