Real-Time Measurement of In-Field Soil Surface Roughness Using Laser and Infrared Sensors

Articles in Press

Document Type : Research Article

Authors

• N. Salehi Babamiri ¹
• H. Haji Agha Alizadeh ¹
• M. Dowlati ²

¹ Department of Biosystems Engineering, Faculty of Agriculture, Bu-Ali Sina University, Hamedan, Iran

² Department of Food Science and Technology, Tuyserkan Faculty of Engineering and Natural Resources, Bu-Ali Sina University, Hamedan, Iran

10.22067/jam.2024.90374.1300

Abstract

Introduction
Soil surface roughness is an important factor in determining the intensity and quality of tillage operations, and obtaining accurate information essential for precision tillage. Using an inappropriate technique due to the lack of precise discrepancy detection can lead to increased time spent on analysis and potential damage. Generally, there are two methods for measuring soil surface roughness: contact and non-contact. Contact methods are less accurate for measuring the roughness of soft soil because they involve physical contact, which can partially disturb the soil. Most non-contact measurement methods are also performed in stop-and-go conditions, which increases measurement time and related analysis. The aim of this study is to measure soil surface roughness in real-time using optical sensors in the field. The accuracy and precision of two non-contact measurement methods will be compared to determine the best approach for precision tillage operations.
Materials and Methods
In the current research, a real-time soil surface roughness measurement system consisting of mechanical and electrical modules, data collection, and processing was built. The system performance was evaluated at different forward speeds and roughness categories, with two types of infrared and laser sensors. To assess the sensors’ accuracy, the collected data was compared against the pin gauge method, which served as the reference standard. The method exhibiting the least variation from this reference is considered to provide the most reliable data. Also, to further examine the accuracy of the sensors, the roughness data obtained from the sensor at various frequencies was compared against the roughness data obtained from the pin measuring device at the same level, resulting in a suitable curve plot. The interpretation of the obtained mathematical relationship indicates the precision of the sensor data.
Results and Discussion
The results obtained from the optical sensors were compared to the pin meter, used as the reference method, in both stationary and moving conditions. It was demonstrated that the optical sensors detect distance in the static state similarly to the reference pin meter. The calibration curve interpretation factor was 0.99 for the infrared sensor and 1 for the laser sensor, indicating a strong correlation between the sensor signals and their distance from the soil surface. The random roughness index was significant for different roughness classes at the 1% probability level, showing that this index effectively distinguishes between the resulting roughness classes. Analysis of variance results revealed that the measurement method had a significant effect at the 1% level. The method with the smallest difference from the reference method is considered the most appropriate measurement technique. The effect of forward speed was also significant at the 1% level; the speed at which the sensor’s performance did not significantly differ from the reference method was identified as the optimal speed for the system. Additionally, the effect of roughness class was significant at the 1% level, confirming that the created roughness classes had meaningful differences. The results of the sensor accuracy evaluation showed that the data obtained from the laser sensor at speeds of 1 and 2.6 km h^-1 had no significant difference with the reference method. Therefore, it is appropriate to use the laser sensor at speeds of 1 and 2.6 km h^-1. At speeds higher than 3.5 km h^-1, the laser sensor successfully detected smooth surfaces, but did not correctly distinguish uneven surfaces. In general, the laser sensor was able to detect all categories of roughness at a speed of 2.6 km h^-1. One reason the laser sensor did not perform well at speeds above 2.6 km h^-1 was its low data acquisition rate. By using laser sensors with a higher data collection rate, the soil height profile can be plotted similarly to a pin scale. The infrared sensor was successful only in detecting smooth surfaces but failed to detect other types of surfaces.
Conclusion
Due to limited accuracy and the risk of damaging or altering the surface roughness, the contact method is not recommended for use on soft soil surfaces. Among non-contact methods, the most suitable technique is the one that provides the highest accuracy and precision while minimizing cost and time for data collection and analysis. In this study, two types of sensors including laser and infrared ranging were selected based on their reasonable price, ease of operation, compatibility with a mobile system, and ability to deliver real-time roughness measurements in the shortest possible time. The results demonstrated that real-time measurement of soil surface roughness can effectively replace traditional, tedious, and time-consuming methods.

Keywords

• Infrared sensor
• Laser sensor
• Precision
• Real-Time measurement
• Surface roughness

Main Subjects

• Precision Farming

References

1. Aguilar, M. A., Aguilar, F. J., & Negreiros, J. (2009). Off-the-shelf laser scanning and close-range digital photogrammetry for measuring agricultural soils microrelief. Biosystems Engineering, 103(4), 504-517. https://doi.org/10.1016/j.biosystemseng.2009.02.010
2. Al-Suhaibani, S. A., & Ghaly, A. E. (2010). Effect of plowing depth of tillage and forward speed on the performance of a medium size chisel plow operating in a sandy soil. American Journal of Agricultural and Biological Sciences,5(3):247-255. https://doi.org/10.3844/ajabssp.2010.247.255
3. Allmaras, R. R., Burwell, R. E., Larson, W. E., & Holt, R. F. (1966). Total porosity and random roughness of the interrow zone as influenced by tillage. USDA Conservation Research Report, 7, 1-14.
4. Amoah, J., Amatya, D. M., & Nnaji, S. (2013). Quantifying watershed surface depression storage: Determination and application in a hydrologic model. Hydrological Processes, 27(17). 2401-2413. https://doi.org/10.1002/hyp.9364
5. Anthonis, J., Mouazen, A. M., Saeys, W., & Ramon, H. (2004). An automatic depth control system for online measurement of spatial variation in soil compaction, Part 3: Design of depth control system. Biosystems Engineering, 89(1), 59-67. https://doi.org/10.1016/j.biosystemseng.2004.06.013
6. ASTM D2216-19. (2019). Standard Test Methods for Laboratory Determination of Water (Moisture) Content of Soil and Rock by Mass. ASTM International, West Conshohocken.
7. Bagheri, M. A. (2023). 3D surface profile extraction using image processing, The nin^th international Conference on Knowledge and Technology of Mechanical, Electrical Engineering and Computer of Iran, Tehran. (in Persian with English abstract).
8. Bauer, T., Strauss, P., Grims, M., Kamptner, E., Mansberger, R., & Spiegel, H. (2015). Long-term agricultural management effects on surface roughness and consolidation of soils. Soil and Tillage Research, 151, 28-38. https://doi.org/10.1016/j.still.2015.01.017
9. Carvajal, F., Aguilar, M. A., Agüera, F., Aguilar, F. J., & Giráldez, J. V. (2006). Maximum depression storage and surface drainage network in uneven agricultural landforms. Biosystems Engineering, 95(2), 281-293. https://doi.org/10.1016/j.biosystemseng.2006.06.003
10. Cierniewski, J., Karnieli, A., Kazmierowski, C., Krolewicz, S., Piekarczyk, J., Lewinska, K., Goldberg, A., Wesolowski, R., & Orzechowski, M. (2015). Effects of soil surface irregularities on the diurnal variation of soil broadband blue-sky albedo. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8(2), 493-502. https://doi.org/10.1109/JSTARS.2014.2330691
11. Cremers, N. H. D. T., Van Dijk, P. M., De Roo, A. P. J., & Verzandvoort, M. A. (1996). Spatial and temporal variability of soil surface roughness and the application in hydrological and soil erosion modeling. Hydrological Process, 10, 1035-1047. https://doi.org/10.1002/(sici)1099-1085(199608)
12. Dalla Rosa, J., Cooper, M., Darboux, F., & Medeiros, J. C. (2012). Soil roughness evolution in different tillage systems under simulated rainfall using a semivariogram-based index. Soil and Tillage Research, 124, 226-232. https://doi.org/10.1016/j.still.2012.06.001
13. Draelos, M., Deshpande, N., & Grant, E. (2012). The Kinect up close: Adaptations for short-range imaging. IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI), pp. 251-256. https://doi.org/10.1109/mfi.2012.6343067
14. Fallahi, E., Aghkhani, M. H., & Bayati, M. R. (2015). Design construction and evaluation of the automatics position control system of tillage tools. Iranian Journal of Biosystem Engineering, 46(2), 117-123. (in Persian with English abstract).
15. García Moreno, R., Díaz Alvarez, M. C., Tarquis Alonso, A. M., Paz Gon´zalez, A., & Saa´ Requejo, A. (2010). Shadow analysis of soil surface roughness compared to the chain set method and direct measurement of micro-relief. Biogeosciences, 7, 2477-2487. https://doi.org/10.5194/bg-7-2477-2010
16. Gilliot, J. M., Vaudour, E., & Michelin, J. (2017). Soil surface roughness measurement: A new fully automatic photogrammetric approach applied to agricultural bare fields. Computers and Electronics in Agriculture, 134, 63-78. https://doi.org/10.1016/j.compag.2017.01.010
17. Gohari, M., Hemmat, A., & Afzal, A. (2010). Design Construction and evaluation of a variable-depth tillage implement equipped with a GPS. Iranian Journal of Biosystem Engineering, 41(1), 1-9. (in Persian with English abstract).
18. Govers, G., Takken, I., & Helming, K. (2000). Soil roughness and overland flow. Agronomie, 20 (2), 131-146. https://doi.org/10.1051/agro:2000114
19. Guzha, A. C. (2004). Effects of tillage on soil microrelief, surface depression storage and soil water storage. Soil and Tillage Research, 76, 105-114. https://doi.org/10.1016/j.still.2003.09.002
20. Jensen, T., Karstoft, H., Green, O., & Munkholm, L. J. (2017). Assessing the effect of the seedbed cultivator leveling tines on soil surface properties using laser range scanners. Soil and Tillage Research, 167, 54-60. https://doi.org/10.1016/j.still.2016.11.006
21. Jester, W., & Klik, A. (2005). Soil surface roughness measurement—methods, applicability, and surface representation. Catena, 64(2-3), 174-192. https://doi.org/10.1016/j.catena.2005.08.005
22. Haubrock, S., Kuhnert, M., Chabrillat, S., Güntner, A., & Kaufmann, H. (2009). Spatiotemporal variations of soil surface roughness from in-situ laser scanning, Catena, 79. 128-139. https://doi.org/10.1016/j.catena.2009.06.005
23. ISO 17892-1. (2014). Geotechnical investigation and testing — Laboratory testing of soil — Part 1: Determination of water content. International Organization for Standardization, 10 pp.
24. Kitchen, N. R., Sudduth, K. A., Drummond, S. T., Scharf, P. C., Palm, H. L., Roberts, D. F., & Vories, E. D. (2010). Ground-based canopy reflectance sensing for variable-rate nitrogen corn fertilization. Agronomy Journal. 102, 71-84. https://doi.org/10.2134/agronj2009.0114
25. Koval, L., Vaňuš, J., & Bilík, P. (2016). Distance measuring by ultrasonic sensor. IFAC-PapersOnLine, 49(25), 153-158. https://doi.org/10.1016/j.ifacol.2016.12.026
26. Kuipers, H. (1957). A reliefmeter for soil cultivation studies. Netherlands Journal of Agricultural Science, 5, 255-262. https://doi.org/10.18174/njas.v5i4.17727
27. Lee, J., Yamazaki, M., Oida, A., Nakashima, H., & Shimizu, H. (1996). Non-contact sensors for distance measurement from ground surface. Journal of Terramechanics, 33(3), 155-165. https://doi.org/10.1016/s0022-4898(96)00016-x
28. Lin, B. B., & Richards, P. L. (2007). Soil Random Roughness and Depression Storage on Coffee Farms of Varying Shade Levels. Agricultural Water Management, 92(3), 194-204. https://doi.org/10.1016/j.agwat.2007.05.014
29. Maleki, M. R., Mouazen, A. M., De Ketelaere, B., Ramon, H., & De Baerdemaeker, J. (2008). On-the-go variable rate phosphorus fertilization based on a VIS-NIR. Biosystems Engineering, 99(1), 35-46. https://doi.org/10.1016/j.biosystemseng.2007.09.007
30. Marinello, P. F., Gasparini, A., Arvidsson, F., & Sartori, J. L. (2015). Application of the Kinect sensor for dynamic soil surface characterization. Precision Agriculture, 16(6), 601-612. https://doi.org/10.1007/s11119-015-9398-5
31. Martinez-Agirre, A., Alvarez-Mozos, J., & Gi´menez, R. (2016). Evaluation of surface´roughness parameters in agricultural soils with different tillage conditions using a laser profile meter. Soil and Tillage Research, 161, 19-30 https://doi.org/10.1016/j.still.2016.02.013 .
32. Marzahn, P., Seidel, M., & Ludwig, R. (2012). Decomposing dual scale soil surface roughness for microwave remote sensing applications. Remote Sensor Journal. 4. 2016-2032. https://doi.org/10.3390/rs4072016
33. Matthias, A. D., Fimbres, A., Sano, E. E., Post, D. F., Accioly, L., Batchily, A. K., & Ferreira, L. G. (2000). Surface roughness effects on soil albedo. Soil Science Society of America Journal, 64(3), 1035-1041, https://doi.org/10.2136/sssaj2000.6431035x
34. Mohammadi, F., Maleki, M. R., & Khodaei, J. (2022). Control of variable rate system of a rotary tiller based on real-time measurement of soil surface roughness. Soil and Tillage Research, 215, 105216 https://doi.org/10.1016/j.still.2021.105216 .
35. Mohammadi, F., Maleki, M. R., & Khodaei, J. (2023). Laboratory evaluation of infrared and ultrasonic range-finder sensors for on-the-go measurement of soil surface roughness. Soil and Tillage Research, 229, https://doi.org/10.1016/j.still.2023.105678
36. Moreno, R. G., Álvarez, M. C. D., Alonso, A. T., Barrington, S., & Requejo, A. S. (2008). Tillage and soil type effects on soil surface roughness at semiarid climatic conditions. Soil and Tillage Research, 98(1), 35-44. https://doi.org/10.1016/j.still.2007.10.006
37. Mouazen, A. M., Maleki, M. R., De Baerdemaeker, J., & Ramon H. (2007). On-line measurement of some selected soil properties using a VIS–NIR sensor. Soil and Tillage Research, 93(1), 13-27. https://doi.org/10.1016/j.still.2006.03.009
38. Nayerifard, T. (2015). Extraction of three-dimensional soil surface profile using laser based on digital image processing, Faculty of Agriculture. Bu Ali Sina University. (in Persian with English abstract).
39. Podmore, T. H., & Huggins, L. F. (1981). An automated profile meter for surface roughness measurements. Transactions of the American Society of Agricultural Engineers, 24(3), 663-665. https://doi.org/10.13031/2013.34317
40. Römkens, M. J. M., Singarayar, S., & Gantzer, C. J. (1986). An automated non-contact surface profile meter. Soil and Tillage Research, 6, 193-202. https://doi.org/10.1016/0167-1987(86)90454-x
41. Saleh, A. (1993). Soil roughness measurement: chain method. Journal of Soil and Water Conservation, 48(6), 527-529. https://doi.org/10.1080/00224561.1993.12456826
42. Sharda, A., Franzen, A., David, E., Clay, J., & Luck, D. (2019). Precision Variable Equipment. Crop Science Society of America, and Soil Science Society of America. P. 155-168. https://doi.org/10.2134/precisionagbasics.2016.0094
43. Smith, M.W. (2014). Roughness in the earth sciences. Earth-Science Reviews, 136, 202-225. https://doi.org/10.1016/j.earscirev.2014.05.016
44. Thomsen, L. M., Baartman, J. E. M., Barneveld, R. J., Starkloff, T., & Stolte, J. (2015). Soil surface roughness: comparing old and new measuring methods and application in a soil erosion model. Soil, 1(1), 399-410. https://doi.org/10.5194/soil-1-399-2015
45. Vermang, J., Norton, L. D., Baetens, J. M., Huang, C., Cornelis, W. M., & Gabriels, D. (2013). Quantification of soil surface roughness evolution under simulated rainfall. Transactions of the American Society of Agricultural Engineers, 56(2), 505-514. https://doi.org/10.13031/2013.42670
46. Weidong, L., Baret, F., Xingfa, G., Qingxi, T., Lanfen, Z., & Bing, Z. (2002). Relating soil surface moisture to reflectance. Remote Sensing of Environment, 81(2-3), 238-246. https://doi.org/10.1016/s0034-4257(01)00347-9
47. Zribi, M., Ciarletti, V., & Taconet, O. (2000). Validation of a rough surface model based on fractional brownian geometry with SIRC and ERASME radar data over orgeval. Remote Sensing of Environment, 73, 65-72. https://doi.org/10.1016/s0034-4257(00)00082-1