Karel Charvat Jr’s scientific contributions

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Publications (28)


Linked Data and Metadata
  • Chapter
  • Full-text available

August 2021

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351 Reads

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Raul Palma

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Data is the basis for creating information and knowledge. Having data in a structured and machine-readable format facilitates the processing and analysis of the data. Moreover, metadata—data about the data, can help discovering data based on features as, e.g., by whom they were created, when, or for which purpose. These associated features make the data more interpretable and assist in turning it into useful information. This chapter briefly introduces the concepts of metadata and Linked Data—highly structured and interlinked data, their standards and their usages, with some elaboration on the role of Linked Data in bioeconomy.

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Fig. 8.3 Mapping of the generic components into cereals and biomass crop pilots in the pipeline view
Fig. 8.5 Mapping of the components used in the use case of linked open EU-datasets in the pipeline view
Fig. 8.6 Mapping of the components used in the use case of linked (meta) data of geospatial datasets in the pipeline view. The components related to the first sub-case (Micka) are highlighted in green, while the components related to the second sub-case (FedEO) are highlighted in orange
Fig. 8.7 Mapping of the components used in the fishery use case in the pipeline view
Fig. 8.11 Overlap area between a plot and a buffer zone of a water body in its vicinity, colored with orange

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Linked Data Usages in DataBio

August 2021

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120 Reads

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1 Citation

One of the main goals of DataBio was the provision of solutions for big data management enabling, among others, the harmonisation and integration of a large variety of data generated and collected through various applications, services and devices. The DataBio approach to deliver such capabilities was based on the use of Linked Data as a federated layer to provide an integrated view over (initially) disconnected and heterogeneous datasets. The large amount of data sources, ranging from mostly static to highly dynamic, led to the design and implementation of Linked Data Pipelines. The goal of these pipelines is to automate as much as possible the process to transform and publish different input datasets as Linked Data. In this chapter, we describe these pipelines and how they were applied to support different uses cases in the project, including the tools and methods used to implement them.




FATIMA Czech pilot

October 2017

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65 Reads

In FATIMA project, a pilot site in Czechia was established to demonstrate how precision agriculture may serve for optimizing crop yields as well as for protection of water quality, since the pilot is located in Czech largest drinking water reservoir catchment. The pilot site Dehtáře is situated in the south-west Bohemo-Moravian Highland. The site contains tile drainage and is of very heterogeneous soil conditions; from shallow, light and stony Haplic Cambisols to heavy Haplic Gleysols, with profoundly different water regimes. For the field trial (spring barley in 2016), crop yield potential was determined from crop statuses as captured by satellite images) eight years back, assessed by Enhanced Vegetation Index. Based on this, as well as on a detailed soil survey and repeated soil sampling, variable fertilizer application zones (70 – 120%) were delineated and mineral fertilizers distributed accordingly with GPS operated spreader three times from late April to late May. The rest of the site was fertilized uniformly. Soil water regime (soil moisture, soil water potential) was monitored continuously on eight spots and real-time broadcasted by wireless sensor network to WEB GIS interface via SensLog solution, adopted from FOODIE project. In the same spots, soil water was sampled by gravitational soil lysimeters. Precise harvest showed a general agreement with the delineated application zones and yield potential, however, some ambiguities were revealed, most probably due to changeable soil water regime, as documented by the sensors, as well as due to variable soil chemical properties (low soil pH). Nevertheless, precisely applied fertilizer doses in the application zones brought about 10% higher crop yields with simultaneous better N crop efficiency. Soil water quality samples confirmed that heterogeneous doses of fertilizer in correctly delineated zones is a promising approach for improvement of groundwater quality especially in shallow soils with low water and nutrient retention ability.


Big Data in Agriculture – From FOODIE towards DataBio

October 2017

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201 Reads

What’s the role of Big Data in the farming ecosystem? Farmers need to measure and understand the impact of a huge amount and variety of data which drive overall quality and yield of their fields. Among those are local weather data, GPS data, ortophotos, satellite imagery, soil specifics, soil conductivity, seed, fertilizer and crop protectant specifications and many more. Being able to leverage this data for running long and short term simulations in response to “events” like changed weather, market need or other parameters is indispensable for farmers in terms of maximizing their profits. IoT (Internet of Technology) including field sensors and machinery monitoring. The experimentation in FarmTelemetry project demonstrates that one average Czech farm (i.e. around 1’000 hectares) could generate daily 20 MegaBytes of data. This could be only for Czech Republic something between 30 and 50 GB per one day. We may easily reach Terabytes of data a day from agricultural basic monitoring by sensors in Europe. Together with satellite data agriculture will need to manage extremely large amount of data. On one side there is growing whole ecosystem with a strong need to secure Big Data from different repositories and heterogeneous sources. In some cases, sharing of data could be common interest, but on other side, there are also different interests and data could help to one part of value chain to take bigger part of profit. From this reason Big data are sensitive topics and trusting of producers about data security is essential. The producers of seeds and chemicals want to maximize their business with farmers. Our team stated implementation of Big Data technologies in frame of European 7FP project FOODIE. This work currently the work continue as part of DataBio project.


Disaster Risk Reduction in Agriculture through Geospatial (Big) Data Processing

August 2017

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1,570 Reads

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33 Citations

ISPRS International Journal of Geo-Information

Intensive farming on land represents an increased burden on the environment due to, among other reasons, the usage of agrochemicals. Precision farming can reduce the environmental burden by employing site specific crop management practices which implement advanced geospatial technologies for respecting soil heterogeneity. The objectives of this paper are to present the frontier approaches of geospatial (Big) data processing based on satellite and sensor data which both aim at the prevention and mitigation phases of disaster risk reduction in agriculture. Three techniques are presented in order to demonstrate the possibilities of geospatial (Big) data collection in agriculture: (1) farm machinery telemetry for providing data about machinery operations on fields through the developed MapLogAgri application; (2) agrometeorological observation in the form of a wireless sensor network together with the SensLog solution for storing, analysing, and publishing sensor data; and (3) remote sensing for monitoring field spatial variability and crop status by means of freely-available high resolution satellite imagery. The benefits of re-using the techniques in disaster risk reduction processes are discussed. The conducted tests demonstrated the transferability of agricultural techniques to crisis/emergency management domains.


DataBio - D1.1 – Agriculture Pilot Definition

June 2017

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577 Reads

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2 Citations

The objective of WP1 Agriculture pilot is to demonstrate how the Big data technologies will be integrated into the pilots, in order to validate the Big data technologies on practical cases from agriculture and how it can fulfil the end user communities’ expectations. The Big technologies will be tested in three areas: arable farming, horticulture and Subsidies an insurance, where every area will be tested in in pilots with different topics and running in different countries. Task 1.1 Co-innovative preparations deal with user understanding specifying the needs of users and different stakeholders and its main objective is to analyse a set of functional and non-functional requirements specified from the analysis of the pilot cases. Opportunities for different solution technologies were reviewed with stakeholders and users are used as an input and a set of scenarios are described within the bio-economy domain related to the agriculture sector. Functional requirements are defined and used as input for the application specification, development and piloting. User and stakeholder study to specify the (most beneficial) areas of interest from different point-of-views and resulting to detailed scenario building of the application scenarios from which use cases are defined. This subtask feeds from user and stakeholder study as input.




Citations (7)


... These sectors face the challenge of increasing productivity without expanding cultivated areas, while also needing to mitigate environmental impacts [2,3]. The integration of advanced technologies, such as sensors, unmanned aerial vehicles (UAVs), georeferencing systems, and artificial intelligence, enables real-time data collection, optimizing the use of inputs like water, fertilizers, and agricultural pesticides, improving the monitoring of crops and animals, and promoting more efficient management of natural resources [4,5]. ...

Reference:

Use of Unmanned Aerial Vehicles for Monitoring Pastures and Forages in Agricultural Sciences: A Systematic Review
Disaster Risk Reduction in Agriculture through Geospatial (Big) Data Processing

ISPRS International Journal of Geo-Information

... The library can render data on the map provided by third party libraries (e.g. OpenLayers, Leaflet, GoogleMap API). Figure 13.3 shows an example for the analysis of yield potential [11]. ...

MONITORING OF IN-FIELD VARIABILITY FOR SITE SPECIFIC CROP MANAGEMENT THROUGH OPEN GEOSPATIAL INFORMATION

The International Archives of the Photogrammetry Remote Sensing and Spatial Information Sciences

... Geospatial information is highly valuable for these purposes [24,25], in particular when based on Semantic web principles [16]. A differentially corrected Global Navigation Satellite Systems (GNSS) equipped yield monitoring system on field harvesters enables collection of georeferenced yield data [10,18]. These data can be processed within Geographic Information system (GIS) using several interpolation techniques in order to generate detailed yield maps [5,19]. ...

MONITORING OF IN-FIELD VARIABILITY FOR SITE SPECIFIC CROP MANAGEMENT THROUGH OPEN GEOSPATIAL INFORMATION

The International Archives of the Photogrammetry Remote Sensing and Spatial Information Sciences

... The need to apply a data-based ecosystem approach in the agricultural sector should encourage sectoral coordination in the development of a common environment, such as an agricultural data sharing platform, and the establishment of legislation on the ways and obligations of sharing and data formats [2]. In the agricultural sector stakeholders must manage many different and heterogeneous sources of information that must be combined to make ecologically and economically sound decisions [10]. ...

Open Data Model for (Precision) Agriculture Applications and Agricultural Pollution Monitoring

... Use cases and requirements of the OLU were defined as a joint effort of eight European research, innovation, and application projects. During the FOODIE project, the OLU dataset was used for various agriculture use cases and extended by agriculture-related data [48]. Semantic models and related ontology based on the Linked Open Data approach were designed during the FOODIE project as well [49] and later extended within the DataBio project [50]. ...

FOODIE - Open data for agriculture

... Rubber tree and oil palm farming are priorities in Nigeria considering glaring evidence of excellent production in the past (Bassey, 2016 andAkujuru, 2015). The need to also preserve the purported arable land for oil palm and rubber tree farming (Ofem et al, 2016) and considering the continuous impacts of environmental conditions on the land (Bassey, 2016) (Charvat, 2010). Close range photogrammetry refers to any photogrammetric mission where the object is not above 300 meters in altitude from the survey platform (Huang et al, 2015). ...

New Geospatial Technologies For Precision Farming