New insights on measuring soil water content with low-cost sensor

ABSTRACT Using sensors to estimate soil water content is important for managing water resources and making decisions on their multiple uses. However, commercial platforms are still extremely expensive, and the development of more accessible systems is essential. This study assesses low-cost capacitive sensors v1.2 to estimate water content in soil using the Arduino platform. Undeformed samples of two different soils (Rhodic Paleudalf and Rhodic Hapludox) were collected at two different depths with three sample volumes. The weight difference data due to sample drainage were collected every 5 min together with the reading values of the soil sensors. To evaluate the obtained results, statistical resources were used, such as Pearson correlation analysis (r), simple linear regression, second-order polynomial regression, root mean square error, Willmott index, and performance index. It was possible to verify that factors such as soil type, sample volume, and the time interval between data collection affect the performance of the sensors. The shortest time intervals between each reading of the sensors showed that soils with sandy texture should be calibrated with second-order equations and with reading intervals not exceeding 24 h.