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

Abstract

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 (R2 ≥ 0.89) was the most appropriate way for modelling the behaviour of the sensors at different soil textures.

Development of a Conservative Hamiltonian Dynamic System for the Early Detection of Leaks in Pressurized Pipelines

“…The challenge lies in selecting an appropriate filter that eliminates noise without significantly affecting the key signal information, particularly the detection of peaks and subpeaks that indicate the presence of leaks in piping systems. For example, Kulmány et al (2023) [23] maintained that calibration could be performed using capacitive sensors. For this reason, the calibration of the sensors was carried out using as a reference a standard average obtained from a pressure manometer of the SCJN series, with a precision of ± 0.5% FS.…”

The Monitoring System of Soil PH Factor Using IoT-Webserver-Android and Machine Learning: A Case Study

“…The capacitive soil moisture sensor (number 7) is an analog sensor that operates to measure soil moisture [36], [34]. Analog values range from 0 -1023.…”

“…The soil moisture sensor outputs an analog value for field data from soil moisture (1 -1023). According to [46], [36] sensor capacitive soil moisture, the analog value category < 350 is damp or wet. Values between 350 -700 are considered typical, whereas values > 700 indicate a dry environment.…”

Complementary Use of Ground-Based Proximal Sensing and Airborne/Spaceborne Remote Sensing Techniques in Precision Agriculture: A Systematic Review

“…Influences of field heterogeneity [164] [67,191] Sensor field placement [64,91,167,175,185,192] Effective sensing [91,170,175,183,[193][194][195][196] Security Data security and governance [165,166,168,182,190,192,194,[197][198][199][200][201][202] Hacking attacks [199,200,202,203] Data processing Data analysis [19,166,188,189 [175,185,186] Latency and throughput [19,166,168,184,211] Future research 5G challenges [167,171] Ground robot path planning and execution [212] Over...…”

“…Another challenge for IoT is the lack of reliable measurements for low-cost ground sensors [175]. For example, the measurement of soil moisture with ground sensors is challenging because the capacitive soil moisture sensors operate at low frequencies and are sensitive to the soil texture and salinity level [193]. The placement of sensors is another important challenge of incorporating IoT.…”