Filters
cris logo
-
New search Filters
Select from list
Displayed from Show more
You have reached the maximum number of displayed search results.
All results displayed
No results are available for the search performed
Displayed from
You have reached the maximum number of displayed search results.
All results displayed
Loading results
Obtaining statistical data

Projects / Programmes source: ARIS

Implementing digital-twins of ecosystems of agricultural lands

Edit
Research activity

Code Science Field Subfield
2.07.03  Engineering sciences and technologies  Computer science and informatics  Programming technologies - software 
4.03.04  Biotechnical sciences  Plant production  Sustainable agriculture 

Code Science Field
1.02  Natural Sciences  Computer and information sciences 
4.01  Agricultural and Veterinary Sciences  Agriculture, Forestry and Fisheries 
Keywords
Digital twin, sustainable agriculture, climate change, Earth Observations, Data Fusion, Environmental Intelligence, geospatial analysis, computer science
Evaluation (rules)
source: COBISS
Evaluation of bibliographic research performance indicators according to ARIS methodology
Citations Citations for bibliographic records in COBIB.SI that are linked to records in citation databases
source: WoS source: Scopus source: COBISS
Researchers (12)
no. Code Name and surname Research area Role Period No. of publications
1.  14979  Damjan Jerič  Plant production  Researcher  2020 - 2023  92 
2.  37447  PhD David Jesenko  Computer science and informatics  Researcher  2020 - 2023  46 
3.  37672  PhD Simon Jurič  Computer science and informatics  Researcher  2020 - 2023  24 
4.  37222  PhD Štefan Kohek  Computer science and informatics  Researcher  2020 - 2023  109 
5.  18590  PhD Iztok Kramberger  Electronic components and technologies  Researcher  2020 - 2023  774 
6.  21318  PhD Bogdan Lipuš  Computer science and informatics  Researcher  2020 - 2023  54 
7.  33709  PhD Niko Lukač  Computer science and informatics  Researcher  2020 - 2023  202 
8.  29243  PhD Domen Mongus  Computer science and informatics  Head  2020 - 2023  277 
9.  15459  MSc Martin Puhar  Computer science and informatics  Researcher  2020 - 2023  64 
10.  08638  PhD Krista Rizman Žalik  Computer science and informatics  Researcher  2020 - 2023  185 
11.  52197  Dino Vlahek  Computer science and informatics  Researcher  2020 - 2023  8 
12.  06671  PhD Borut Žalik  Computer science and informatics  Researcher  2020 - 2023  850 
Organisations (3)
no. Code Research organisation City Registration number No. of publications
1.  0796  University of Maribor, Faculty of Electrical Engineering and Computer Science  Maribor  5089638003  27,531 
2.  1394  Kmetijsko gozdarska zbornica Slovenije Kmetijsko gozdarski zavod Murska sobota (Slovene)  Murska Sobota  5129940000  424 
3.  1504  IGEA, svetovanje in storitve s področja nepremičnin, infrastrukture in prostora, d.o.o. (Slovene)  Brezovica pri Ljubljani  5336236000  296 
Abstract
Digital twins have become a major technology trend and a critical component in the implementation of smart environments. With their ability to mimic the behaviour of real-world entities in virtual environments, they provide advanced monitoring, diagnostics, prognostics, and optimization capacities. However, as their implementation requires convergence of many technologies and non-technical aspects, ranging from Internet of-things to artificial Intelligence with integrated domain-specific knowledge, their usage today is limited to highly controlled environments, such as smart factories and smart homes. Their immense potentials to provide environmental intelligence, thus, remain unutilised, specially, when considering protection of Earth from degradation through sustainable management of its natural resources and urgent actions against climate changes. Today, food production is amongst the main producers of greenhouse gases (GHG), while being under immense pressure due to the rapid urbanisation. It is, therefore, critical to address the trade-off between safeguarding food production, while lowering GHG emissions. This can only be achieved by deepening understanding of our interactions with agricultural ecosystems. The proposed project addresses contemporary challenges of digital twins for modelling such socio-environmental interactions by providing significant advances beyond state-of-the-art in the following aspects: A new in-situ sensory system, capable of simultaneously capturing CO2, N2O and CH4 emissions, together with temperature and moisture of surroundings as well as levels of plant photosynthesis using quantum sensor with location data provided by Galileo,A data harvesting system, intended for gathering and aligning IDEAL’s in-situ data with open Earth Observation data sources (e.g. Copernicus satellite images, GEOSS thematic maps, and LiDAR data from Slovenian environmental agency) for common representation of spatiotemporal entities,An advanced data fusion framework designed for mining IDEAL’s data sources by the principles of deep and feature learning for spatiotemporal extrapolations and crop-growth simulations,Process optimization and visual analytics services for providing for prescriptive analytics capacities of socio-environmental interactions with the support of explainable artificial intelligence. As a result, IDEAL digital twin shall enable: Monitoring of farmers’ interaction with agricultural ecosystems,Diagnostics of green-house-gas emissions, soil health, and crop development parameters,Prognostics of their changes during the time, andOptimization of farming processes, accordingly. In accordance with user-centric design, project development shall be governed by three complementary pilots, each addressing the specifics of a particular agricultural ecosystem that all together cover 98% of Slovenian farmland, namely, grasslands, arable lands, and permanent crops. Within each of the pilots, systematic data collections shall be conducted periodically during crop and grass growth, before and after all major farming activities, including tillage, fertilization, planting, and harvesting in order to ensure accurate profiling of the following parameters: High-resolution GHG emission that includes CO2, N2O and CH4. Soil health parameters and derived nutrition levels, as for example fertility indices, pH, and manganese, and Crop and grass development parameters based on their physical features like levels of photosynthesis productions and growth. In order to maximize the project potentials, IDEAL digital twin shall be plugged-in into existing precision farming infrastructure provided by industrial partner (namely Igea d.o.o.), turning natural ecosystem into a smart environment. IDEAL shall, thus, provide the necessary social innovation infrastructure to the researchers and practitioners that are currently struggling with low level of general digitalization in agricultural sector.
Confirmation required
Views history
Favourite