Abstract
Optimal use of farming machinery is important for efficiency and sustainability. Continuous automated control of the machine settings throughout the tillage operation requires sensory feedback estimating the seedbed quality. In this paper we use a laser range scanner to capture high resolution maps of soil aggregates in a laboratory setting as well as full soil surface maps in a field test. Gaussian curvature is used to estimate the size of single aggregates under controlled circumstances. Additionally, a method is proposed, which cumulates the Gaussian curvature of full soil surface maps to estimate the degree of tillage.
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References
Braunack, M., Dexter, A.: Soil aggregation in the seedbed: a review i. effect of aggregate sizes on plant growth. Soil Tillage Res. 14(3), 259–279 (1989)
Braunack, M., Dexter, A.: Soil aggregation in the seedbed: a review ii. effect of aggregate sizes on plant growth. Soil Tillage Res. 14(3), 281–298 (1989)
Frede, H.G., Gäth, S.: Soil surface roughness as the result of aggregate size distribution 1. report: measuring and evaluation method. Zeitschrift für Pflanzenernährung und Bodenkunde 158(1), 31–35 (1995)
Friedman, A., Pizarro, O., Williams, S.B., Johnson-Roberson, M.: Multi-scale measures of rugosity, slope and aspect from benthic stereo image reconstructions. PLoS ONE 7(12), e50440 (2012)
Gray, A.: Modern Differential Geometry of Curves and Surfaces with Mathematica, 2nd edn. CRC Press, Boca Raton (1997)
Håkansson, I., Myrbeck, Å., Etana, A.: A review of research on seedbed preparation for small grains in Sweden. Soil Tillage Res. 64(1), 23–40 (2002)
Helming, K., Roth, C.H., Wolf, R., Diestel, H.: Characterization of rainfall-microrelief interactions with runoff using parameters derived from digital elevation models (dems). Soil Technol. 6(3), 273–286 (1993)
Jensen, T., Munkholm, L.J., Green, O., Karstoft, H.: A mobile surface scanner for soil studies. In: Second International Conference on Robotics and associated High-technologies and Equipment for Agriculture and Forestry, pp. 187–194 (2014)
Jester, W., Klik, A.: Soil surface roughness measurement - methods, applicability, and surface representation. Catena 64(2), 174–192 (2005)
McDonald, A., Crossley, S., Bennet, J., Brown, S., Cookmartin, G., Morrison, K., Quegan, S.: Stereo vision measurements of soil surface characteristics and their use in model validation. In: SAR workshop: CEOS Committee on Earth Observation Satellites. vol. 450, p. 575 (2000)
Scharstein, D., Szeliski, R.: High-accuracy stereo depth maps using structured light. In: 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition 2003, vol. 1, pp. I–195. IEEE (2003)
Stafford, J.V.: Implementing precision agriculture in the 21st century. J. Agric. Eng. Res. 76(3), 267–275 (2000)
Zribi, M., Ciarletti, V., Taconet, O., Boissard, P., Chapron, M., Rabin, B.: Backscattering on soil structure described by plane facets. Int. J. Remote Sens. 21(1), 137–153 (2000)
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Jensen, T., Munkholm, L.J., Green, O., Karstoft, H. (2015). Soil Surface Roughness Using Cumulated Gaussian Curvature. In: Nalpantidis, L., Krüger, V., Eklundh, JO., Gasteratos, A. (eds) Computer Vision Systems. ICVS 2015. Lecture Notes in Computer Science(), vol 9163. Springer, Cham. https://doi.org/10.1007/978-3-319-20904-3_48
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DOI: https://doi.org/10.1007/978-3-319-20904-3_48
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