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A multi sensor data fusion approach for creating variable depth tillage zones

Published online by Cambridge University Press:  01 June 2017

D. Whattoff ,
A. Mouazen  and
T. Waine
D. Whattoff*
Affiliation:
SOYL, Kennetside, Newbury, Berks, UK
A. Mouazen
Affiliation:
Cranfield University, College Road, Cranfield, Bedfordshire, UK
T. Waine
Affiliation:
Cranfield University, College Road, Cranfield, Bedfordshire, UK
*
†Email: david.whattoff@soyl.co.uk
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Abstract

In this research a multi-sensor and data fusion approach was developed to create variable depth tillage zones. Data collected with an electromagnetic sensor was fused with measurements taken with a hydraulic penetrometer and conventionally acquired soil bulk density (BD) and moisture content (MC) measurements. Packing density values were then calculated for eight soil layers to determine the need to cultivate or not. From the results 62% of the site required the deepest tillage at 38 cm, 16% required tillage at 33 cm and 22% required no tillage at all. The resultant maps of packing density were shown to be a useful approach to map layered soil compaction and guide VDT operations.

Keywords

Variable depth tillagedata fusionbulk densitypacking density
Type
Tillage and Seeding
Information
Advances in Animal Biosciences , Volume 8 , Special Issue 2: Papers presented at the 11th European Conference on Precision Agriculture (ECPA 2017), John McIntyre Centre, Edinburgh, UK, July 16–20 2017 , July 2017 , pp. 461 - 465
Copyright
© The Animal Consortium 2017 

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References

Adamchuk VI, Hummel JW, Morgan MT and Upadhyaya SK 2004. Computers and electronics in agriculture 44, 71–91.CrossRefGoogle Scholar
Chamen, T 2006–2011. Minimising the area of soil compacted by traffic in cropping system. PhD thesis. Cranfield University, UK.Google Scholar
Domsch, H, Ehlert, D, Giebel, A, Witzke, K and Boess, J 2006. ‘Evaluation of the soil penetration resistance along a transect to determine the loosening depth’. Precision Agriculture 7, 309–326.CrossRefGoogle Scholar
Fulton JP, Wells LG, Shearer SA and Barnhisel RI 1996. Spatial variation of soil physical properties: a precursor to precision tillage. ASAE Paper No. 961012. St Joseph, Michigan, USA.Google Scholar
Gill WR,Vanden Berg GE 1968. Soil dynamics in tillage and traction. Agricultural Handbook no. 316. U.S. Government Printing Office., Washington, D.C. Google Scholar
Halcro, G 2013. Site specific land management of cereal crops based on proximal soil sensing. PhD thesis. Cranfield University, UK.Google Scholar
Kaufmann, M 2008. Multi criteria evaluation of land restoration for agricultural use (PhD Thesis). Swiss Federal institute of Technology, Zurich.Google Scholar
Keskin, SG, Khalilian, A, Han, YJ and Dodd, RB 2011. ‘Variable-depth tillage based on georeferenced soil compaction data in Coastal Plain Soils’. International Journal of Applied Science and Technology 1, 22–33.Google Scholar
Mouazen, AM and Nemenyi, M 1999. Tillage Tool Design by the Finite Element Method: Part 1. Finite Element Modelling of Soil Plastic Behaviour. Journal of Agricultural Engineering Research 72 (1), 37–51.CrossRefGoogle Scholar
Renger, M 1970. Uber den Einflus der Dränung auf das Gefüge und die Wasserdurchlässigkeit bindiger Böden. Mitteilungen Deutschen Bodenkundlich Gesellschaft 11, 23–28.Google Scholar