ارزیابی عملکرد حسگرهای فاصله‌یاب نوری در اندازه‌گیری بلادرنگ زبری سطح خاک

نوع مقاله : مقاله پژوهشی

نویسندگان

1 گروه مهندسی بیوسیستم - دانشکده کشاورزی - دانشگاه بوعلی سینا - همدان - ایران

2 گروه علوم و مهندسی صنایع غذایی، دانشکده فنی و منابع طبیعی تویسرکان، دانشگاه بوعلی سینا، همدان، ایران

چکیده

اندازه‌گیری زبری سطح خاک‌های کشاورزی نقش مهمی در ارزیابی عامل‌های مختلف از جمله: عملکرد ادوات خاک‌ورزی، حفظ آب سطحی، آماده‌سازی بستر بذر و مدیریت رواناب سطحی دارد. روش‌های مرسوم اندازه‌گیری زبری اغلب از رویکرد توقف و حرکت استفاده می‌کنند که هم خسته‌کننده و هم زمان‌بر است. در صورت اندازه‌گیری بلادرنگ زبری خاک، زمان اندازه‌گیری به میزان قابل توجهی کاهش خواهد یافت و راه انجام اقدام‌های کارآمدتر در کشاورزی دقیق هموارتر می‌شود. در این مطالعه عملکرد حسگرهای نوری مادون قرمز و لیزری روی یک سیستم متحرک مورد ارزیابی قرار گرفت. نتایج نشان داد که سرعت دستگاه، تأثیر معنی‌داری بر عملکرد سامانه نداشت. اثر متقابل روش اندازه‌گیری و کلاس زبری در سطح یک درصد معنی‌دار بود. یک ارتباط قوی بین زبری‌ به‌دست آمده از پین‌متر و حسگر لیزری در سرعت‌های پیش‌روی کم‌تر ازkmh-1 5/3 (R² > 0.9) مشاهده شد، با وجود این‌که در سرعت kmh-1 8/4 مقدار ضریب تبیین مدل برازش به 79/0 کاهش یافت؛ اما تا حد زیادی موفق شد زبری واقعی را پیش‌بینی کند. این مطالعه نشان داد که استفاده از حسگرهای لیزری با نرخ جمع‌آوری داده‌های بالاتر می‌تواند تشخیص کلاس‌های زبری را تسهیل کند و ترسیم نقشه پروفیل خاک را شبیه به روش پین‌متر امکان‌پذیر نماید. روش مادون قرمز در سطوح با پستی و بلندی منظم، اختلاف معنی‌داری با روش پین‌متر نداشت ولی در سطوح نامنظم، در سطح یک درصد اختلاف معنی‌داری با روش پین متر داشت. یافته‌های تحقیق حاضر، بر پتانسیل حسگرهای نوری برای اندازه‌گیری سریع زبری خاک تأکید نمود.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

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

نویسندگان [English]

  • Nassim Salehi Babamiri 1
  • Hossein Haji Agha Alizadeh 1
  • Majid 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
چکیده [English]

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

کلیدواژه‌ها [English]

  • Real-time Soil Surface Roughness Measurement
  • Optical Range-Finder Sensors
  • Variable Rate Tillage
  • Random Roughness
Abbaszadeh, P., Moradkhani, H., and Zhan, X. (2019). Downscaling SMAP radiometer soil moisture over the CONUS using an ensemble learning method. Water Resources Research, 55(1): 324–344. https://doi.org/10.1029/2018WR023354.
Aguilar, M. A., Aguilar, F. J., and 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.
Alam, A. M., Farhad, M. M., Kurum, M., and Gurbuz, A. (2024). An Advanced Testbed for Passive/Active Coexistence Research: A Comprehensive Framework for RFI Detection, Mitigation, and Calibration. 2024 United States National Committee of URSI National Radio Science Meeting (USNC-URSI NRSM). 9-12 Jan 2024. Boulder, Colorado, USA. P. 280. https://doi.org/10.23919/USNC-URSINRSM60317.2024.10464436.
Allmaras, R. R. (2024). Total porosity and random roughness of the interrow zone as influenced by tillage. USDA Conservation Research Report, 7: 1-14.
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.
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.
Anthonis, J., Mouazen, A. M., Saeys, W. and 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.
Bagheri, M. A. (2023). 3D surface profile extraction using image processing, The ninth international Conference on Knowledge and Technology of Mechanical, Electrical Engineering and Computer Of Iran, Tehran, Iran. P. 6. (In Persian).
Bauer, T., Strauss, P., Grims, M., Kamptner, E., Mansberger, R., et al., (2015). Longterm 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.
Carrara, M., Comparetti, A., Febo, P., Orlando, S. (2004). Spatially variable herbicide application on Durum wheat in Sicily. Biosyst. Eng, 87: 387–392. https://doi.org/10.1016/j.biosystemseng.2004.01.004.
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.
Cierniewski, J., Karnieli, A., Kazmierowski, C., Krolewicz, S., Piekarczyk, J., et al., (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.
Crummett, D., (2019). Variable intensity tillage offers solutions for varying soil conditions. In Niche Equipment Markets, Manufacturer News available at: https://www.farm-equipment.com/articles/16770-variable-intensity-tillage-offers-solutions-for-varying-soil-conditions.
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.
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) 13-15 September,  Hamburg, Germany. pp. 251–256. https://doi.org/ 10.1109/MFI.2012.6343067.
Fallahi, E., Aghkhani, M. H., and 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).
https:// doi.org/10.22059/ijbse.2015.55669.
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, 2010.
Gilliot, J. M., Vaudour, E., Michelin, J., (2017). Soil surface roughness measurement: A new fully automatic photogrammetric approach applied to agricultural bare fields. Computer and Electronic in Agriculture. 134: 63–78. https://doi.org/10.1016/j.compag.2017.01.010.
Gohari, M., Hemmat, A., and 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).
https:// doi.org/ 20.1001.1.20084803.1389.41.1.1.0
Govers, G., Takken, I., & Helming, K. (2000). Soil roughness and overland flow. Agronomie, 20(2): 131-146.
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
Haubrock, S., Kuhnert, M., Chabrillat, S., Güntner, A., and Kaufmann, H. (2009)."Spatiotemporal variations of soil surface roughness from in-situ laser scanning," Catena, 79.128–139. ttps://doi.org/10.1016/j.catena.2009.06.005.
Kitchen, N. R., Sudduth, K. A., Drummond, S. T., Scharf, P. C., Palm, H. L., et al., (2010). Ground-based canopy reflectance sensing for variable-rate nitrogen corn fertilization. Agron. J, 102: 71–84. https://doi.org/10.2134/agronj2009.0114.
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.
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.
Koval, L., Vaňuš, J., and Bilík, P. (2016). Distance measuring by ultrasonic sensor. IFAC-PapersOnLine, 49(25): 153-158. https://doi.org/10.1016/j.ifacol.2016.12.026.
Kuipers, H. (1957). A reliefmeter for soil cultivation studies. NJAS. 5(4). https://doi.org/10.18174/njas.v5i4.17727.
Lee, J., Yamazaki, M., Oida, A., Nakashima, H., Shimizu, H., (1996). Non-contact sensors for distance measurement from ground surface. J. Terra, 33 (3): 155–165. https://doi.org/10.1016/S0022-4898(96)00016-X
Lee, K. H. and Ehsani, R. (2008). Comparison of two 2D laser scanners for sensing object distances, shapes, and surface patterns. Computers and Electronics in Agriculture, 60(2): 250–262. https://doi.org/10.1016/j.compag.2007.08.007.
Lin, B. B. and 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.
Liu, L.; Bi, Q.; Zhang, Q.; Tang, J.; Bi, D.; Chen, L. (2022). Evaluation Method of Soil Surface Roughness after Ditching Operation Based on Wavelet Transform. Actuators, 11: 87. https://doi.org/ 10.3390/act11030087.
Marinello, F., Pezzuolo, A., Gasparini, F., Arvidsson, J., & Sartori, 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.
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.
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.
Marzahn, P., Seidel, M., Ludwig, R. (2012). Decomposing dual scale soil surface roughness for microwave remote sensing applications. Remote Sens. J, 4: 2016–2032. https://doi.org/10.3390/rs4072016.
Matthias, A. D., Fimbres, A., Sano, E. E., Post, D. F., Accioly, L., et al., (2000). Surface roughness effects on soil albedo. Soil Science Society of America Journal, 64(3): 1035–1041, https://doi.org/ 10.2136/sssaj2000.6431035x.
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.
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 Tillage Res, 229: 105678. https://doi.org/10.1016/j.still.2023.105678.
Nayerifard, T. (2015). Extraction of three-dimensional soil surface profile using laser based on digital image processing. MS thesis, biosystem engineering, Bu Ali Sina University, The Iran. (In Persian with English abstract).
Podmore, T. H. and 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.
Römkens, M. J. M., and Wang, J.Y. (1986). Effect of tillage on surface roughness. Transactions of the American Society of Agricultural Engineers, 29(2): 429–433.