3. AI applications for Earth Observation-based crop field risk management

Mentor: Kristina Šermukšnytė-Alešiūnienė

‘AgriFood Lithuania’ (www.agrifood.lt) is a Digital Innovation Hub (DIH) that brings together major research, business and public stakeholders in Lithuania for the common mission of transforming agriculture, food and associated sectors with digital-based innovations.

The mission of AgriFood Lithuania DIH is to contribute towards achieving the vision outlined in the EU Declaration of ‘A smart and sustainable digital future for European agriculture and rural areas’. As the only agriculture and food-focused DIH in Lithuania, the Hub is extensively working with and promoting breakthrough technology applications in AgriFood. Key areas of technological expertise include Artificial Intelligence, Internet of Things, remote sensing, Blockchain and Robotics. The DIH links its stakeholders with international and cross-sector initiatives to provide all-round support in the research, development and deployment of AgriFood Tech innovations, as well as strengthening the national and European technological infrastructure.

The DIH is participating in national and international agriculture and food focused innovation development and support projects, both directly and through its member organizations. AgriFood Lithuania is participating in the EU large-scale project SmartAgriHubs (www.smartagrihubs.eu) and acts as a national coordinating DIH in the organizational structure. The DIH is also co-organizing the international conference for business and policy leaders the ‘AgriBusiness Forum’ (www.digitalfarm.lt), and ‘Hack AgriFood’ (www.hackagrifood.lt) – the first hackathon in Lithuania focused on agriculture and food technology.Challenge Description: Exploratory solutions for AI model applications in various crop monitoring and disease risk management using satellite imagery and spectral data

2. Agroclimatic map of selected region

Mentor: Pavel Hájek

The main components of EUXDAT & Stargate Climatic data processing HUB are the following:

  • EUXDAT Portal: It is the entrance point to EUXDAT functionalities. It provides a web GUI which gives access to the workflow execution tool, monitoring of data analytics execution, the catalogue and other useful tools;
  • Identity and Authorization Manager: This component is responsible of managing user accounts and managing access to the functionalities and data in EUXDAT, according to security policies and to the rights granted to each user;
  • Data & Algorithms Catalogue: It keeps a record of all the algorithms, applications and datasets which are available in EUXDAT;
  • Data & Algorithms Repository: This component deals with the storage of datasets, algorithms and images, in general, that will be used for running data analyses;
  • Data Manager: It is the component in charge of moving data to the proper location. It will configure and operate extraction APIs for accessing several data sources. For doing so, it also has all the data connectors that are necessary;
  • SLA Manager: It agrees on quality attributes to fulfil and the values to be met for each attribute. It also retrieves information about the monitoring of such attributes in order to detect SLA breaches;
  • Orchestrator: It deals with the management of resources, mainly from the functional perspective, deploying the algorithms and the corresponding data in the optimal location. It also deals with the application profiles generation and management;
  • Monitoring: It retrieves information about the resources execution and status, as well as about the algorithms execution and datasets status.

Challenge Description: Agroclimatic map of selected region

This challenge is focused on how the temperature changes through time can provide invaluable information for broad number of professionals in agriculture, environmental scientists or historians.  The example of such a map is an Agroclimatic Atlas Of Canada, particularly e.g. a map of Fall Freeze Dates: Average Dates of First Fall Freeze. 

This challenge is about data processing, data analysis and model-based producing of detailed agroclimatic data of a region based on more coarse data (weather, topography, hydrology, soil type and so on).

1. Mechelen Pilot – TraMod/Spotbooking integration

Mentor. Daniel Beran 

Traffic Modeller (TraMod) is a tool for transport modeling developed in collaboration between traffic engineers, IT and GIS specialist. It can be fully implemented in server environment with an application programming interface for mobile and web applications. This creates an opportunity for a city or a region government representatives to test various traffic scenarios within seconds without a need to install and learn how to use desktop traffic modelling software or contacting traffic engineers every time a new roadwork appears in the region.Challenge Description: Mechelen Pilot – TraMod/Spotbooking integration