The latest agricultural machinery from CNH Industrial’s Case IH and New Holland Agriculture brands incorporates OS’s OS Net signal technology, based on a national network of global navigation satellite system (GNSS) reference stations.
The developments by CNH Industrial mean that vehicles using the system seamlessly switch between reference stations as they move between different coverage areas. The continuous, real-time “RTK” (real-time kinematic) data this provides allows the farming machinery to be positioned on a farm to centimetre-level accuracy.
“The result is a guidance system that provides optimum signal availability no matter what the topography, eliminating the inaccuracy, downtime and stress that can be caused by signal loss,” says Ross Macdonald, Case IH AFS specialist.
MacDonald continues: “Customers benefit from the most advanced correction signal technology available, with a network that is the largest of its type in Europe, providing broader coverage than that available from other private names in the industry and bringing unrivalled accuracy and signal dependability benefits.
There’s no need for the operator to manually switch from one reference station to another if the signal from the first station should drop out. The Case IH AFS+ system uses a roaming SIM card, which ensures that it always looks for the best available mobile phone network.”
Precision farming and machine automation are already taking on ever-greater importance to meet the need for more efficient, economic and environmentally-friendly agriculture.
When used for the precise positioning of farm machinery, the OS Net partnership with CNH Industrial increases efficiencies and optimises yields throughout the complete crop cycle of planting, spraying, harvesting and cultivation. For example, you can plan and manage seed sowing more precisely, reducing waste and reducing costs. It also allows drivers to continue to work with reliable accuracy in poor field or operator conditions.
The total crop area in the UK stood at 6.1 million hectares in 2015. If we consider cereals and oilseeds as an example, the UK cereal crop area was approximately 3.1 million hectares in 2015 and the area of oilseed crop planted was 670,000 hectares. This totals 3.77 million hectares. If all of the UK’s cereal and oilseed were planted manually without RTK, assuming a general industry overlap error of 4%, there would be an overall loss of 70,800 hectares, equivalent to over 113,000 football pitches.
If on the other hand that whole area was planted with RTK, the error decreases from 4% to 0.33%, reducing the overlaps by 64,959 hectares, leading to total potential savings of over £34 million depending upon fuel, seed and fertiliser inputs.
OS is also a part of the Atlas consortium, funded by Innovate UK, the government’s Centre for Connected and Autonomous Vehicles, and Dept. BEIS. Atlas is identifying the data and communication requirements to make Britain a global leader in driverless car technologies and innovation.
OS’s Chief Geospatial Scientist, Jeremy Morley, says: “GPS is fine for most uses in giving an idea of location, but the signal between satellites and the end user on Earth is disrupted by the atmosphere and inaccuracies are produced. For driverless, for example, you need something more accurate. OS Net, with its 110 stations based around the country, monitors these signals and produces corrections to give the extra degree of accuracy.”
Morley continues: “Autonomous vehicles on public highways will need to find their way reliably and safely through a vast network of streets while interacting with driven and other autonomous vehicles. For this to happen we need that degree of precision OS Net produces, plus detailed mapping about the locality about the car to inform the car where it is and where it is going.
“With the Atlas project we are looking at the data requirements needed and the communication links in the locality on the vehicle and at how much data can be realistically passed across this network. It is a very interesting interplay between our social fabric governance and this technical infrastructure. How will these cars work? What is the intelligence needed within them to work out not only where they are and where they’re going, but what other drivers are doing around them and how to interact with other people?”
Notes to Editors
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