1. Collecting farm related machinery and sensor data in the cloud-based platform
V. Lukas1, T. Řezník2, K. Charvát jr.3, K. Charvát3, Z. Křivánek3, Š. Horáková3, M. Musil3, J. Měkotová4
1Mendel University, Faculty of Agronomy, Department of Agrosystems and Bioclimatology, Brno, Czech Republic
2Masaryk University, Faculty of Science, Department of Geography, Brno, Czech Republic
3 WirelessInfo, Litovel, Czech Republic
4 MJM Litovel, a.s., Czech Republic

2. Introduction
Nowadays various data could be obtained by common farm management. Traditionally, in a plant production such data comprises information about fields, soil conditions and crop treatments. Moreover, data for a plant production also includes sensor data recorded from a variety of stationary and mobile devices such as farm machines, crop sensors, weather stations, etc.
A cloud platform for collection, storage, sharing and analysis of large quantities of spatially and non-spatially referenced data is being developed in the European project “Farm-Oriented Open Data in Europe” (FOODIE).
A collection of data was verified within the FOODIE Czech pilot farm with 1’214 ha of arable land to obtain information about farm machinery management and agro-meteorological observation. Selected tractors and implements were equipped by telemetry units to record vehicle trajectory in the fields and a wireless sensor network was established to observe meteorological conditions within a two fields with cereals. For such purposes, a novel data model was developed to manage both sensor data and farm records within one platform simultaneously with the client application, which allows end-users to make visualization and analysis of farm data.

3. Czech pilot
Farm Vajglov
MJM Litovel
Farm Tršice

4. Farm Tršice Farm Tršice
located in most productive region in Czech republic
intensive crop production on arable land + hops production
Average elevation of fields
Average year temperature
Total amount of precipitation per year
Total area
Arable land
Grassland
Orchards
Organic farming
Farm Tršice
284 m
8.9°C
570 mm
1’291 ha
1’214 ha -
74 ha (hopfields) NO

5. Farm Tršice
red = arable land yellow = hopfields

6. Sc.B – Machinery Telematics
Main purposes of these scenario are:
Evaluation of the economic efficiency of machinery operations within the fields.
Precise records of crop management treatments (fertilizers, pesticides).
Improved management of machinery operations and planning of crop management treatments.
Control of quality of field operations, such as pass-to-pass errors and overlaps, coverage of maintained area, recommended work speed.
Control of applied input material in comparison to prescribed rates.
Compliance of agro-environmental limits (Nitrate Directive, Good Agricultural and Environmental Conditions - GAEC, protection of water resources, etc.).

7. Tractor Art

8. Farm Telemetry
The effectiveness of each production, including agriculture, is determined by the ratio of the value of the production outputs to the value of production inputs. One of the possibilities of solving the farm effectiveness problem,
FarmTelemetry focuses on is to optimize the level of farm inputs. It can be the energy needed to power agricultural machinery on the fields, energy for the transport of inputs and outputs of production

9. Machinery Monitoring

10. Machinery monitoring

11. Tractor movement

12. Fuel consumption: tillage (l/h)

13. Fuel consumption: detail (l/h)

14. Work Log: Excel export

15. Daily time utilization (Excel export)

16. Sc.C – Site Specific Crop Management
3. Meteorological monitoring
to capture detailed dynamics of weather conditions on the ground.
Weather data will be recorded at the specific localities in high frequency (between 10 and 15 minutes).
The main goal is to obtain data for modelling of crop growth and to support decision making by agronomist for plant protection (prediction of the plant pests and diseases infestation), plant nutrition (crop growth and nutrient supply), soil tillage (soil moisture regime) and irrigation (soil moisture).

17. Czech Pilot Execution Progress
Activities in Scenario C
Design and development of sensor network
Deployment and testing of sensor network at pilot farm
meteorological station at farm pitch
deployment of WSN gateway and nodes over selected fields (winter wheat and spring barely) – measurement of soil moisture, temperature, EC and air temperature + humidity

18. WSN field instalation
(reality)
WSN design (planned)

20. FOODIE Data Models

21. User needs One platform for all the most common tasks
Production planning
Production monitoring, alerting and analyses
Subsidies management
Environmental burden monitoring
Ownership of farmer’s data
Farmer’s data are private and sensitive data
Remains farmer’s property
Modularity
Customizable and scalable platform

22. FOODIE Data Models Core Data Model Management zones Interventions
Treatments …

23. Core Data Model Data Model compliant to: Open and scalable
Directive 2007/2/EC (INSPIRE)
ISO standards series
Open and scalable

24. Core Data Model Holding attribute Value Identifier
Function
agriculture
User identifier
Name
Tršická zemědělská, a.s.
Valid From
Begin Lifespan

25. Core Data Model Site attribute Value Identifier
Activity (NACE code)
A Growing of cereals (except rice), leguminous crops and oil seeds
Valid From
Begin Lifespan

26. Core Data Model – Site “Site” level is the lowest INSPIRE-defined one
However, differently defined within various INSPIRE spatial data themes (issue addressed by the INSPIRE clusters)
Key for the integration to a Land Parcel Identification System (LPIS)
Typically managing Ministry of Agriculture of each Member State
45 LPIS’ within 28 European Member States
Some countries have LPIS connected to the cadaster, some not
Basic level for subsidies
Successful integration of the Czech LPIS within the Pilot 3 of the FOODIE project

27. Core Data Model Plot attribute Value Identifier
Valid from
Origin type
manual
Crop species
wheat …

28. Core Data Model Plot attribute Value Identifier
Valid from
Origin type
manual
Crop species
wheat …

29. Core Data Model Intervention attribute Value Type tillage Status
ongoing
Intervention start
Supervisor
John First, senior manager, phone 7435 …

30. Core Data Model Treatment attribute Value Intervention type
herbicide application
Status
ongoing
Intervention start
Supervisor
John First, senior manager, phone 7435
Treatment quantity
70 litres
Application width
25 meters
Form of treatment
Application machine
Product
Roundup® …

31. Core Data Model Treatment attribute Value Intervention type
herbicide application
Status
ongoing
Intervention start
Supervisor
John First, senior manager, phone 7435
Treatment quantity
70 litres
Application width
25 meters
Form of treatment
Application machine
Product
Roundup® …
Product attribute
Value
Product code
Product name
Roundup®
Product type
herbicide
Manufacturer
MONSANTO®
Register URL
Safety instructions
Eye contact: may cause may cause pain, redness and tearing based on toxicity studies. …

32. Core Data Model

33. Supportive data Separate data stores Satellite and aerial images
Data model according to the data source
Core data mode stores user-specified (interpreted) data
Satellite and aerial images
Data harvester component as a part of the FOODIE cloud
LANDSAT 8 and later on Sentinel data imported into the platform immediately as available
Including the basic processing like computation of vegetation indices
Volunteer geographic information (VGI)
Lightweight profile of the core model to be developed within the second year of the project

34. FOODIE Data Models Core Data Model Sensor Data Model Management zones
<om:result>
<swe:DataArray>
<swe:elementCount>
<swe:Count>
<swe:value>5</swe:value>
</swe:Count>
</swe:elementCount>
<swe:elementType name="Components">
<swe:DataRecord>
<swe:field name="Time">
<swe:Time definition="
<swe:uom xlink:href="
</swe:Time>
</swe:field>
<swe:field name="feature">
<swe:Text definition="urn:ogc:data:feature"/>
<swe:field name="temperature">
<swe:Quantity definition="urn:ogc:def:phenomenon:tom:temperature">
<swe:uom code="°C"/>
</swe:Quantity>
<swe:field name="latitude">
<swe:Quantity definition="urn:ogc:def:latitude">
<swe:uom code="decimalDegrees"/>
<swe:field name="longitude">
<swe:Quantity definition="urn:ogc:def:longitude">
</swe:DataRecord>
</swe:elementType>
Core Data Model
Management zones
Interventions
Treatments …
Sensor Data Model
ISO O&M
also VGI
<swe:encoding>
<swe:TextEncoding decimalSeparator="." tokenSeparator=","
</swe:encoding>
</swe:DataArray>
</om:result>

35. FOODIE Data Models Core Data Model Sensor Data Model
Road segment attributes
Identifier, e.g.
National code, e.g. 43
Road width, e.g. 5 meters
Maximum height, e.g. 2.1 meters
Maximum weight, e.g. 2 tons
Speed limit, e.g. 20 km·h-1
Surface category, e.g. asphalt, unpaved,…
HAZMAT limitation, i.e. flammable/explosive/corrosive/toxic/oxidizing
Core Data Model
Management zones
Interventions
Treatments …
Sensor Data Model
ISO O&M
also VGI
Transport Data Model
OSM for navigation
edge
vertex
edge
edge
vertex
vertex
edge
edge
edge