Projects / Programmes
Implementing digital-twins of ecosystems of agricultural lands
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 |
Digital twin, sustainable agriculture, climate change, Earth Observations, Data Fusion, Environmental Intelligence, geospatial analysis, computer science
Researchers (12)
no. |
Code |
Name and surname |
Research area |
Role |
Period |
No. of publicationsNo. 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)
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.