Autonomous Robotised Repeatable Soil Moisture Sampling

In this study, we present the continuation of the novel robotic mechanism introduced at the EGU General Assembly Conference 2025 (I. S. Tsimpidi, 2025) for autonomous soil moisture data collection. Soil moisture is vital for irrigation, flood and drought forecasting, and hydrological studies, yet shows strong spatial and temporal variability; therefore accurate measurements are required. We conduct field experiments to improve the fully autonomous robotized procedure with AgriOne, reducing sampling time and enhancing repeatability.
As the AgriOne robot, Figure 1, enables in situ, high-precision, spatially dense data collection across the field, we conducted additional field experiments to collect soil moisture data, both autonomously and manually. The AgriOne robot autonomously executes soil moisture data-collection missions, with sampling positions defined by georeferenced waypoints. The waypoints were generated in ArcGIS software using a grid creation tool, with the centre of each grid square as the selected position. The size of each grid cell was defined as 8m * 8m. In the sequel, these waypoints feed the robotic autonomous navigation system, which combines satellite positioning and motion sensors to continuously estimate the robot’s position and plan its trajectory and the sampling points, to meet the initially planned sampling protocol. For the robotic navigation, a hierarchical control architecture generates velocity commands to guide the robot to each target location with centimeter-level positioning accuracy. Upon reaching each waypoint, the system autonomously triggers a probing mechanism to collect and log soil moisture measurements before continuing to the next mission point. Manual data collection was performed by a human carrying a handheld TEROS 12 sensor connected to a Bluetooth sensor interface for instant readings in a mobile application. The positions for taking the measurement were selected using an empirical sampling method.

Figure 1: AgriOne robot with description of its components.

The first experiment was executed successfully in mid-July in a flat field with no vegetation cover, no precipitation, relative air humidity of 52%, air temperature of 20 °C and wind speed of 2 Bft. The autonomous data collection yielded data from 69 of the 73 waypoints where the robot stopped, and the manual data collection yielded data from 50 waypoints, both covering an area of 4.800m2. The second experiment was successfully conducted in mid-October in an area with low elevations and dense grass cover. On the experimental day, precipitation was absent; air temperature was 12°C, relative air humidity was 66% and wind speed was 1 Bft. In this experiment, AgriOne autonomously collected soil moisture data from 63 of the 72 waypoints where stopped, and from 41 waypoints we collected soil moisture data manually, covering an area of 4.700 m2. The results of the conducted experiments are presented on a satellite map of the tested areas, with proportioning the points based on the soil moisture values and are shown in Figure 2.

Figure 2:Presentation of the SM collected data autonomously and manually in both testing areas.

Tsimpidi, I. S. (2025). Large-scale Soil Moisture Monitoring: A New Approach. EGU General Assembly Conference Abstracts, pp. EGU25-1910.