The Farm of the Future: The Digital Farm

Geschreven door Wim Lagae op

Digitization is everywhere today, even in farming. SAP has worked out a model for the digital farm – the farm of the future.

Farming is an industry in which prices are continuously under pressure. The only way for farmers to survive is to cut costs. Automation is one way to do that, as it helps reduce the cost of manpower. Cattle farmers have already gone a long way in automating processes: milking and feeding the livestock, for example, are automated and a warehouse management solution helps keep track of the inventory, i.e. the milk.

In agricultural farming, automation takes a different form. It all starts, of course, with the tractors on the field. Nowadays, these contain sensors that capture information from the engine and from all the farm machinery that is coupled to the tractor (for example, to fertilize the soil).

The data collected by these sensors open the door to innovation and new ways of working resulting in growth and profitability.

1Let’s take John Deere as example. The renowned manufacturer of farm machinery created a new, innovative business model together with SAP. The Deere tractors transmit sensor data via telematics to a central system based on SAP HANA. These sensors indicate not only where the tractor is being used (geolocation), but also how intensively it is being used. Indications of excessive fuel consumption, vibrations and engine data allow John Deere to build a business model of predictive maintenance.

The digital farm allows us to go one step further still. Sensors positioned in the field measure the moisture level, the amount of nitrates and the crop growth rate in real time and forward this data via telematics to a central HANA server.

The sensor data, as a whole, is the basis for continual innovation. By linking field data, tractor data, weather forecasts, historical data and the type of crop that grows in the field, the farmer gets a real-time view of the seasonal tasks that are required (like fertilizing). And when the work is done, he can immediately see the profitability of each individual field.

2How does it work? The technology used inside SAP HANA is `GeoFencing’. Each field has a GeoFence on the map, which collects the data from that field and from the tractors/harvesters and then presents it in a simplified front-end. Via analytical algorithms, tasks are suggested to the farmer. The farmer can then choose to execute the tasks himself or call in a subcontractor, who he can search for in the Fieldglass business network. Fieldglass makes a complex task like finding and planning a subcontractor with a specific type of machinery, easy.

As soon as the subcontractor’s tractor enters the field’s GeoFence, the farmer sees on his mobile device that the work has started. In addition, the data from the tractor in the GeoFence is available in real time for further analysis.

This provides the farmer with important information, such as:

  • When was the task started and finished?
  • How much fertilizer was used on which part of my field?
  • What was the fuel consumption for every meter of ploughing?

Having the data available in real time provides new insights and ensures a smooth planning. Moreover, the subcontractor can bill the farmer immediately after the work on a performance basis rather than a price per ha.

3Also, from an analytical point, the farmer receives valuable insights into the full profitability of each field. The costs and profit of each individual field can be calculated and visualized on the fly, even per square meter, based upon the data from the field and the data from the tractor.

Too far-fetched for Belgian farms? Maybe, but imagine that a producer of fertilizers could provide this as a service to all its customers. The application could even propose tailor-made fertilizers for certain crops and soil types.

Have a look at my video to see how it works.

4

This particular scenario was developed by SAP and Luciad. Headquartered in Leuven, Luciad builds software components that allow mission-critical organizations to deliver geospatial situational awareness applications. In this farming example, the data that the tractor collects is sent and visualized in real time while the machine does its work. The result is a `heatmap’ of information, with an easy-to-interpret, visual delineation.

 

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