Enhancing the Accuracy of the Low-Cost Capacitive Soil Moisture Sensor SEN0193 for Internet of Things Applications
Year: 2025
Type: article
Abstract: Monitoring soil moisture is an essential aspect of comprehending the functioning of terrestrial ecosystems. However, the cost of commercial sensors often challenges their acquisition. In response to this issue, manufacturers have developed several low-cost soil moisture sensors in recent decades, but there is still a need to improve the evaluation of their precision and accuracy. This article assesses the precision and accuracy of a low-cost soil moisture sensor SEN0193. The assessment results highlight the consistent precision of SEN0193 and emphasize the homogeneity of responses among sensors of the same model. The study also underscores the significant impact of soil granulometry on sensor precision, emphasizing the importance of realizing site-specific calibrations. In terms of accuracy, the study reveals that the calibration recommended by the manufacturer tends to overestimate moisture, particularly in soils with smaller granulometry. The study suggests that soil-specific calibration methods utilizing linear equations are more reliable and effective than polynomial and exponential approaches, especially when employing three to five calibration points. This study offers an essential contribution to the advancement of accessible methods for soil moisture monitoring. These methods are crucial for efficiently monitoring soil moisture in agriculture and natural ecosystems.
Institution: Universidade Federal do Rio de Janeiro
Language: English
Topic: Smart Agriculture and AI
Subfield: Plant Science
Domain: Life Sciences
SDG: Zero hunger
Open Access status: diamond
OpenAlex ID: W4409490649