Evaluating the Performance of Optical Range Finder Sensors in Real-time Measurement of Soil Surface Roughness

Introduction
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. The purpose of this study is to measure soil surface roughness in real time using optical sensors in greenhouse conditions and compare the accuracy and precision of the two measurement methods in order to choose the appropriate method in precision tillage operations.
Materials and Methods
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. The purpose of this study is to measure soil surface roughness in real time using optical sensors in greenhouse conditions and compare the accuracy and precision of the two measurement methods in order to choose the appropriate method in precision tillage operations.
Results and Discussion
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), root mean squared error (RMSE), and mean absolute percentage error (MAPE) of 0.98, 2.3, and 2.7 for the infrared (IR) sensor, and 1, 0.2, 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. 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 kmh-1. However, at speeds exceeding 3.5 kmh-1, 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. The results of variance analysis show that, speed did not have a significant effect on the roughness index. A strong correlation (R² > 0.9) was noted between roughness measurements from the pin meter and laser sensor at forward speeds below 3.5 kmh-1, while this correlation decreased to 0.79 at 4.8 kmh-1. Although the predictive power of the fitted model decreased at forward speeds of 4.8 kmh-1, it was largely successful in predicting the roughness class of the soil. 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 kmh-1, 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.
Conclusion
Selecting the appropriate range-finder sensor is essential for online SSR measurement. The findings of this research suggest that the rapid measurement of soil surface roughness can replace traditional, labor-intensive methods, streamlining the process and enhancing accuracy in precision tillage operations.
Acknowledgement
The authors would like to express their gratitude to Bu-Ali Sina University for their support of the present research