The DataBio Machinery management solution is focused mainly on collecting telematic data (farm telemetry) from tractors (Zetor) and other farm machinery to analyse and compare to other farm data. The main goal is to collect and integrate data and receive comparable results. A challenge associated with this pilot is that a farm may have tractors and other machinery from manufacturers that use different telematic solutions and data ownership/sharing policies.
Zetor took a several steps in direction, which aim to expand the capabilities Zetor tractors to precision agriculture services.
The starting point of Zetor tractors on the way to precision agriculture services was solution used by Zetor mainly for the purposes of monitoring reliability of different tractor components. This monitoring is carried out in Zetor's testing area and mainly on third-party farms in real operation, when the farmers gave their consent to data collection. Zetor is extending telematics solution in two ways, customer care with predictive maintenance planning and precision agriculture.
Although the technological principle of collecting data for this type of monitoring and monitoring for the purposes of precision farming and the assessment of economic efficiency is the same, it differs in the settings of various parameters and in the way of working with the data.
Another condition that is required for some operations in precision agriculture is automatic guidance of tractors. Zetor was testing compatibility of terminals and steering systems with selected models of Zetor tractors.
The specific solution collects mainly data from tractors wherever they are working, so farm data are available only for part of the farms, where the tractors are in operation. The Machinery Management solution is able to transform sensor data from the SensLog service (used by FarmTelemeter service) to Linked Data on the fly.
The main component of the solution are:
- Service for the collection, processing and publication of sensor data. SensLog is required by FarmTelemeter service.
- Extension of SensLog for processing, analysis and publication of data from mobile sensor units. Tractors are considered to be a mobile sensor unit
- Visualisation of data from tractors and other farm data.
- Transformation of the Linked Data from the mapping file of SensLog data and publishing the data on the fly
- Linking data from tracts with other farm data.
Agricolture
The agriculture sector is of strategic importance for the European society and economy. Due to its complexity, agri-food operators have to manage many different and heterogeneous sources of information. Agriculture is facing many economic challenges in terms of productivity or cost-effectiveness, as well as an increasing labour shortage partly due to depopulation of rural areas. Current systems still have significant drawbacks in areas such as flexibility, efficiency, robustness, sustainability, high operator cost and capital investment. Furthermore, reliable detection, accurate identification and proper quantification of pathogens and other factors, affecting plant health, common agriculture policy, insurance, are critical to be kept under control so as to reduce economy expenditures, trade disruptions and even human health risks. Agriculture requires collection, storage, sharing and analysis of large quantities of spatially and non-spatially referenced data. These data flows currently hinder the adoption of precision agriculture as the multitude of data models, formats, interfaces and reference systems in use result in incompatibilities. In order to plan and make economically and environmentally sound decisions a combination and management of information is needed.
In many cases farms or agriculture service organizations owns tractors of more than one brand/family. Although the communication protocols used in control units of farm machinery and data collection are subject of standardization, the telematics solutions including data ownership/usage policy are usually specific to each tractor brand/family and the level. Furthermore, attention shall be paid to ISO and CEN standards regulating data sharing in agriculture basing on the input coming from industry organizations like CEMA and AEF.
Although this is not issue and can be even desirable for purposes of tractor producer’s customer care responsible for solving technical problems on tractor, for farmers it can be hard or impossible to connect the data coming from tractor with other farm data relevant for agronomical / economical evaluation of machinery usage. Despite the fact tractor have telematics solution, farmer sometimes need to use third party device and software to obtain data for field specific analysis
For that reason, the Zetor Machinery Management solution will:
- Support development and implementation of Zetor’s telematics solution
- Support authorized users’ access to telematics data using tractor manufacturer’s build-in telematics solution or third-party monitoring tools (depends on tractor manufacturer data ownership/usage policy.)
- Support integration of tractor and agricultural machinery data with other relevant farm data and interfaces for data import into FMIS
- Extracting comparable information from data coming from various tractors of different manufacturers
- Evaluation of economic efficiency of tractor/machinery usage and crop profitability.
From technical point of view the monitoring system involves tracking of the vehicles’ position using GPS combined with acquisition of information from on-board terminal (CAN-BUS) and their online or offline transfer to GIS environment. Such systems collect large amounts of data. The monitoring system will be done in large, medium-sized and small farms based on the level of information processing and their interaction with other farm data, three use cases will be handled.
We map the source sensor data to some semantic vocabularies on the fly and make it accessible as linked data (e.g., via Sparql queries). In particular, this task involved the following steps:
- Data modelling was one of the key tasks performed to transform the sensor data. After an extensive analysis of available vocabularies and ontologies to represent sensor data and measurement, we selected the following:
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- SSN or Semantic Sensor Network is an ontology for describing sensors and their observations, the involved procedures, the studied features of interest, the samples used to do so, and the observed properties. A lightweight but self- contained core ontology called SOSA (Sensor, Observation, Sample, and Actuator) was actually used in this case to align the SensLog data. Link to the ontology is https://www.w3.org/ns/sosa/.
- Data Cube Vocabulary and its SDMX ISO standard extensions were effective in aligning multidimensional survey data like in SensLog. The Data Cube encompasses well known RDF vocabularies (SKOS, SCOVO, VoiD, FOAF, Dublin Core).