Catalogue of Services

Computer Vision for automated food quality assessment

Universitat de Lleida

Validation of an existing automated food quality assessment solution based on artificial intelligence, in particular computer vision techniques. The service will help the solution provider check its performance using a controlled testing infrastructure, including our Food Processing Pilot Plant. The service might also include the generation of reference datasets of food samples and expected quality measures, which can be used both for validation and training of the artificial intelligence solution based on computer vision.

AI model training

Lukasiewicz - Poznanski Instytut Technologiczny

Conformity assessment and compliance tests

As part of this service, we test and measure, among other things, mechanical, physical, acoustic, radio, electromagnetic compatibility and electrical parameters for the purpose of assessing the overall user safety of agricultural, horticultural, forestry and food machinery, equipment and components. Verification is based on the requirements of standards and directives declared by the manufacturer. We carry out an initial assessment based on the design documentation provided or/and on measurements taken on a prototype based on harmonised and non-harmonised standards with relevant EU and sectoral legislation. This is to ensure that for instance essential requirements coming from for instance New Legislative Framework directives and other EU law are fulfilled to better protect both consumers and professionals from unsafe products to be placed on the European internal market. One of the aims is to help manufacturers in legal placing agrifood products on EU single market and in CE-marking process. The results obtained can be used in the further process of product labelling, declaration of conformity to affix the CE mark to the device within the scope of EMC, LVD, RED, MD, MR on other NLF directives and regulations.

Certification

Conformity assessment

Container images have become the de facto standard for software delivery in a variety of industries, including agrifood. Packaging an application and all its dependencies in a container image ensures consistency across different deployment environments, allowing these applications to be tested more quickly and efficiently. Once the container image is built, it can be run by different container management tools, for example, Docker or Podman.

Container image generator that facilitates efficient software testing and deployment

However, creating secure and optimised container images is often complex. This service simplifies the process by automatically generating container images from the application source code. It detects dependencies and environmental requirements, removing the need for the customer to perform and/or to possess technical expertise in container technologies.
The process is fully automated: the user provides the source code reference with secure access, and the service handles the rest. This eliminates the need to manage image creation files and reduces potential human error during the image creation phase, allowing developers to focus on innovation and functionality.

Instituut voor Landbouw-, Visserij- en Voedingsonderzoek

Crop Dataset Generation

In the Crop Dataset Generation service, we collect large-scale datasets on a frequent basis using drones or autonomous robots. AI and data-driven solutions require extensive data, shifting the focus from limited feature engineering to acquiring large, balanced datasets of images and labels for training convolutional deep-learning networks. We offer flexible data collection options tailored to customer needs. We can use our systems and fields to gather data on crops under various conditions, such as different lighting, angles, growth stages, and soil types. Alternatively, we can use customer-provided devices to collect data for testing and validation, such as evaluating sensor performance in detecting heat in crops or testing robot navigation in crop fields.

Provision of datasets

Data Analysis for Smart Irrigation Systems

This service allows companies to experiment with real-time data and advanced analytics to improve water management, reduce waste, and enhance crop yield. The service includes comprehensive testing and evaluation to ensure compatibility and performance, ensuring efficient irrigation practices and sustainability through insights into soil moisture, weather conditions, and plant water needs.

Data analysis

Dataspace use case design analysis

Creating a dataspace involves significant costs and time investment. Technology-related expenses include evolving and adapting reference software components (such as participant agents (connectors), identity and access management mechanisms, and data catalogues), designing the dataspace, and deploying it in a public or private cloud infrastructure. To justify these costs, a compelling value proposition for each data space participant (consumers or providers) is crucial.
The primary aim of the service is to assist the customer in designing well-defined dataspace use cases. The service offers aid in the definition of the purpose, review of the participant roles (that could be data provider, data consumer, data intermediary, or part of the data space governance authority), business and governance models outline, and specification of the reference architecture and user interface of the dataspace.
Tools used in this process include the Starter Kit for Data Space Designers [1], Data Cooperation Canvas [2], Use Case Playbook [3], and Use Case Blueprint [4].

[1] https://dssc.eu/download/attachments/29523973/DSSC-Starterkit-Version-1.0.pdf?download=true
[2] https://www.datacooperationcanvas.eu/canvas/intro
[3] https://internationaldataspaces.org/wp-content/uploads/dlm_uploads/use-case-playbook.pdf
[4] https://dataspacessupportcentre.atlassian.net/wiki/spaces/BVE/pages/357074241/Use+Case+Development

Desk assessment

Evaluation and testing on the efficiency of SaaS platforms

University of Cordoba

Evaluation and analysis of the efficiency of algorithms integrated into SaaS platforms that perform specific processes, such as the calculation of parameters from processed data. The primary objective is to detect weak points in the performance of the platform and evaluate the execution times.

Data analysis

People training

Evaluation for Ethical, Legal, and Social Aspects (ELSA Scan) for responsible AI development

Wageningen Research - WR

What are the ethical, legal, and social aspects (ELSA) of AI-driven technology in agri-food? Mitigation of potential risks and finding opportunities are easier by identifying ELSA aspects in early development stages, when the technology is still in the making. Examples of ELSA aspects are autonomy, transparency, and bias, but also data privacy and other legal aspects. How does your AI technology deal with ELSA aspects? A combined survey and interview help AI developers to identify opportunities and issues with ELSA in the agri-food context from the perspective of end users and more broadly, for society. For example, to better align with the sustainability objectives of the AI-driven technology. The outcomes of the ELSA scan are a list of identified key ELSA aspects and high-level recommendations to further improve the AI technology.

ELSA assessment

Evaluation of AI-based optimal pesticide dosage in tree crops

Universitat de Lleida

Evaluation of the performance of systems to optimise the dosage of pesticides based on artificial intelligence for tree crops. The service includes comparing the optimised treatment performance versus the standard procedure, measuring the pesticide and economic savings, assessing drift reduction to the environment, and improvements in environmental and biodiversity impact or the CO2 footprint of the treatment.

Evaluation of crop classification at parcel scale functionality in an agricultural crop management platform

University of Cordoba

The functionality of crop classification at the parcel level will be assessed using satellite imagery through a platform specialised in agricultural crop management. To evaluate quality, CAP data from prior agricultural seasons will be utilised. This validation process will be applied in Andalusia, Spain, with a focus on the region's agroclimatic conditions and crop phenology.

Data analysis