Determination of the surface topography in rill erosion by imaging techniques

Year 2022, Volume: 64 Issue: 2, 104 - 116, 30.12.2022

Kürşad Erdoğan , Fikret Arı

https://doi.org/10.33769/aupse.1190584

Abstract

Soil erosion, mainly occurring in agricultural areas, is an economic and ecological problem that can happen anywhere. Swelling and transport of soil particles reduce the productivity of agricultural lands. Soil surface analysis and soil-water interaction are essential topics in agricultural research and engineering as they affect the risk of soil erosion. Erosion affects the upper soil layers rich in organic matter. After the transport of this topsoil, the subsoil with a more compact structure emerges. In this case, the cultivation of the soil becomes complex, and agricultural productivity is adversely affected. Different techniques have been used to analyze the effects of erosion. In this study, we focused on rill erosion, one of the types. An electronic imaging system has been designed using the Microsoft Kinect Sensor and Raspberry Pi, which can be found quickly and at a low cost during operation. The software has been developed to extract the surface topography by analyzing the depth images of rill erosion obtained with this system. Measurements were taken using eight types of flow rates on four soil types. As a result of the experimental findings, it has been seen that volume changes of 1.3812 mm3 can be detected as a unit with the Kinect Sensor placed at a distance of 70 cm.

Keywords

Rill erosion, depth image, kinect sensor, raspberry pi, mobile systems, surface topography

References

• Rieke-Zapp, D. H., Nearing, M. A., Digital close range photogrammetry for measurement of soil erosion, Photogramm. Rec., 20 (109) (2005), 69-87.
• Foldager, F. F., Pedersen, J. M., Skov, E. H., Evgrafova, A., Green, O., LiDAR-Based 3D scans of soil surfaces and furrows in two soil types, Sensors, 19 (3) (2019), 661, https://doi.org/10.3390/s19030661.
• Stefano, C. D., Palmeri, V., Pampalone, V., An automatic approach for rill network extraction to measure rill erosion by terrestrial and low-cost unmanned aerial vehicle photogrammetry, Hydrol. Process., 33 (2019), 1883-1895, https://doi.org/10.1002/hyp.13444.
• Eitel, J. U. H., Williams, C. J., Vierling, L. A., Al-Hamdan, O. Z., Pierson, F. B., Suitability of terrestrial laser scanning for studying surface roughness effect on concentrated flow erosion processes in rangelands, Catena, 87 (3) (2011), 398-407, https://doi.org/10.1016/j.catena.2011.07.009.
• Li, P., Ucgul, M., Lee, S. H., Saunders, C., A new method to analyse the soil movement during tillage operations using a novel digital image processing algorithm, Comput. Electron. Agric., 156 (2019), 43-50, https://doi.org/10.1016/j.compag.2018.11.009.
• Saygın, S. D., Süreç tabanlı modellemede parmak erozyonu toprak duyarlılığı parametresinin fiziksel olarak belirlenmesi, (2020). Program Kodu: 3001 Başlangıç AR-GE. Proje No: 1180111.
• Sharma, K., Kinect sensor based object feature estimation in depth images, IJSIP, 8 (12) (2015), 237-246, http://dx.doi.org/10.14257/ijsip.2015.8.12.23.
• Mobini, A., Behzadipour, S., Foumani, M. S., Accuracy of kinect’s skeleton tracking for upper body rehabilitation applications, Disabil. Rehabilitation. Assist. Technol., 9 (4) (2013), 344-352, https://doi.org/10.3109/17483107.2013.805825.
• Pinto de Oliveira, H. F., An Affordable and Practical 3D Solution for the Aesthetic Evaluation of Breast Cancer Conservative Treatment, (2013). Ph.D. Thesis, Faculdade De Engenharia Da Universidade Do Porto, Programa Doutoral em Engenharia Electrotecnica e de Computadores, Porto.
• Mallick, T., Das, P. P., Majumdar, A. K., Characterizations of noise in kinect depth images: a review, IEEE Sens. J., 14 (6) (2014), 1731-1740, https://doi.org/10.1109/JSEN.2014.2309987.