Real-time soil surface roughnes measurement using optical range-finder sensor

Abstract Surface roughness measurements of agricultural soils play a critical role in assessing various factors, including tillage performance, surface water retention, soil resistance to rainfall-induced failure, seedbed preparation, and surface runoff management. random roughness serves as a reliable vertical index due to its ease of calculation and a margin of uncertainty of approximately ±3 mm, making it suitable for distinguishing roughness classes. Roughness measurement methods can be categorized into contact and non-contact techniques. Traditional methods often employ a stop-and-go approach, which is both tedious and time-consuming. In contrast, optical range finder sensors, when mounted on a moving system, can measure soil surface roughness in real-time, significantly reducing measurement time and increasing efficiency. This study explores both contact and non-contact measurement methods, highlighting the advantages of using optical range finder sensors mounted on a mobile system for real-time SSR assessment. Following sensor calibration, the relationship between the distances measured by the sensors and the reference pin meter method demonstrated a linear correlation under stationary conditions, with coefficients of determination (R²), mean squared error (MSE), and mean absolute percentage error (MAPE) of 0.98, 5.6, and 2.7 for the infrared (IR) sensor, and 1, 0.04, and 0.36 for the laser sensor, respectively. Both range-finder sensors effectively measured distances under stationary conditions (R² > 0.98). The performance of the IR and laser optical sensors was further evaluated on a moving system, revealing a significant effect of measurement methods and surface class (p < 0.01) on the standard deviation (SD) roughness index, while machine speed did not significantly affect the results. The interaction between measurement method and surface class was also significant (p < 0.01). The laser sensor was able to accurately detect roughness classes akin to the pin meter method at speeds below 2.6 km/h. However, at speeds exceeding 3.5 km/h, the laser sensor could only identify softer roughness classes, failing to measure roughness indices greater than 1.11 cm due to a decrease in data collection rates and the presence of larger clods in rougher classes. A strong correlation (R² > 0.9) was noted between roughness measurements from the pin meter and laser sensor at forward speeds below 3.5 km/h, while this correlation decreased to 079 at 4.8 km/h. The study suggests that utilizing laser sensors with higher data collection rates could facilitate the detection of roughness classes and enable soil profile mapping akin to the pin meter method, regardless of forward speed. Conversely, the IR method performed well only on wide and regular surfaces and struggled with irregular roughness levels, with R² values of 0.74, 0.69, 0.69, and 0.7 at forward speeds of 1, 2.6, 3.5, and 4.8 km/h, respectively. Consequently, at higher speeds, both the laser and IR sensors exhibited reduced compatibility with the pin meter method. The findings emphasize the potential of optical sensors for rapid SSR measurement, paving the way for more efficient practices in precision agriculture.