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Labelling and Data Augmentation Framework Support

Instituut voor Landbouw-, Visserij- en Voedingsonderzoek (ILVO)

This service provides essential support for creating a robust framework to label, store, and manage data effectively for AI training purposes. Proper labelling is crucial, as deep learning models rely on consistent, high-quality data to detect complex patterns. Within this service, we do not label data ourselves but rather offer training guidelines to ensure that data is labelled accurately and consistently across large datasets. Please refer to other services if you wish for your data to be labelled by us. In addition, we help set up the necessary digital infrastructure for storing and maintaining this data. Our support streamlines the labelling and data augmentation process, laying the foundation for successful AI development in applications such as crop monitoring, pest detection, and yield prediction in the agri-food sector.

Life cycle assessment for AI and robotic solutions

Lukasiewicz Poznanski Instytut Technologiczny (L-PIT)

This service offers a comprehensive environmental analysis of the entire life cycle of your agri-food products, processes, or services, with a particular focus on innovative AI and robotic solutions. Our Life Cycle Analysis (LCA) gathers detailed data on natural resource extraction, environmental emissions, and fuel and energy consumption, specifically related to technologies tested within the agrifoodTEF project – such as AI-powered facilities, autonomous vehicles, and other robotic systems. This enables a robust assessment of environmental impacts across categories including climate change, ozone depletion, and resource scarcity. We also conduct a full carbon footprint analysis, focusing on greenhouse gas emissions and energy use directly linked to the operation and performance of these AI and robotic technologies. To support this process, we offer both general and technical assistance, guiding clients through each step of the LCA. The results help identify opportunities for resource optimisation and waste reduction, ultimately supporting informed business decisions based on tested, cutting-edge solutions. Additionally, our carbon footprint assessment facilitates voluntary emissions reporting and the development of decarbonisation strategies using secure, cloud-based data collection.

Collection of test data

Life Cycle Assessment (LCA) for Agriculture Robots

Danish Technological Institute (DTI)

Life Cycle Assessment (LCA) for Agriculture Robots is a comprehensive evaluation method that assesses environmental impacts at every stage of a robot’s lifecycle—starting from raw material extraction, manufacturing, utilization, and ending with disposal or recycling. This service provides valuable insights into energy consumption, waste generation, and other environmental factors, helping customers identify key areas for improvement. By promoting sustainability and eco-efficiency, LCA supports decision-making for reducing ecological footprints and ensuring compliance with sustainability standards such as Frugal AI requirements. The service is tailored to meet diverse customer needs, enabling them to understand and optimize the environmental performance of their robotic technologies.

LCA assessment

Life cycle assessment (LCA) of AI, Robotic & Digitalisation (AIRDIG) services in the agrifood sector

RISE - Research Institutes of Sweden

This service helps you understand how AIRDIG technologies impact the environment throughout their use in food production. From building the tech infrastructure to using and eventually discarding AI and robotic systems, this assessment pinpoints key areas like energy use, electronic waste, and resource consumption. It also explores how smart farming tools—like automated machines and precision farming—can improve environmental sustainability by reducing waste, conserving resources, and boosting efficiency. This approach offers a clear roadmap for making agrifood innovations more environmentally friendly.

LCA assessment

Life cycle assessment (LCA) of agrifood products with and without AI, Robotic & DIGitalisation (AIRDIG) services

RISE - Research Institutes of Sweden

LCA of agrifood products provides a clear comparison of the environmental impacts associated with food production using advanced technologies like AI, robotics, and digital tools versus traditional methods. By examining every stage of the production process—from resource extraction to processing, packaging, and distribution—this assessment highlights how these innovative technologies can lead to significant reductions in energy consumption, waste generation, and overall environmental footprints. For example, AI can optimise crop management, while robotics can streamline harvesting, both contributing to more efficient resource use. This information empowers businesses to understand the tangible benefits of adopting AIRDIG services, enabling them to make informed decisions that not only enhance environmental sustainability but also improve operational efficiency and reduce costs. Ultimately, the LCA serves as a valuable tool for stakeholders looking to implement greener practices in their agrifood operations.

LCA assessment

Livestock Dataset Generation

Instituut voor Landbouw-, Visserij- en Voedingsonderzoek (ILVO)

Location

Belgium

Livestock farming

This service supports the generation of high-quality datasets tailored for validating and improving AI-driven technologies or sensors designed for livestock management. Whether you are developing new tools for monitoring, controlling, or working with livestock, we help collect and process relevant data that reflects real-world conditions. These datasets enable you to fine-tune your AI models and sensor systems, ensuring they operate accurately and effectively. By leveraging this service, you can optimise your solutions for livestock health monitoring, behaviour analysis, and automated control systems in the agri-food sector.

Collection of test data

Desk assessment

Performance evaluation

Provision of datasets

Test design

Test execution

Test setup

Measuring and testing the static rollover threshold for robotics machinery by tilt table

University of Cordoba (UCO)

This service utilises a progressive tilt platform to conduct stability testing on self-propelled vehicles and robotics machinery, determining essential parameters of rollover stability. The testing infrastructure is designed to validate the effectiveness of rollover prevention systems, ensuring that machinery meets safety and operational standards. Ideal for arable farming, tree crops, and viticulture, this service provides clients with critical insights into the stability performance of their robotics equipment under varying tilt conditions.

Certification

Collection of test data

Data analysis

Desk assessment

Performance evaluation

Test design

Test execution

Test setup

Model training of AI agrifood-related algorithms

The AI model training service in TEF infrastructure offers a powerful platform for developing cutting-edge artificial intelligence solutions in the agrifood sector. Leveraging high-performance computing resources, this service enables innovators to train AI models using diverse tabular data types. These include sensor readings (such as soil moisture, temperature, and nutrient levels), machine-generated data (from agricultural equipment and IoT devices), crop yield statistics, weather patterns, and supply chain metrics. Users can either provide their own datasets or utilise existing data within the TEF infrastructure (see Related Services). The service supports a wide range of AI frameworks and model architectures, allowing for flexible experimentation and rapid iteration. From predictive maintenance of farming equipment to optimising crop management practices, this platform accelerates the development of AI-driven solutions that address critical challenges in agriculture and food science, fostering innovation and efficiency in the industry.

AI model training

Monitoring of deployed AI models for agrifood applications

Our monitoring service provides comprehensive visibility into your own current model deployments. Through an intuitive dashboard interface, stakeholders can track and analyse their model's performance in real-time. The service continuously monitors three critical aspects: computational resource utilisation (such as processing power and memory usage), incoming data streams and patterns, and the model's output results and accuracy. This visibility allows teams to proactively identify potential bottlenecks, optimise resource allocation, and ensure the model performs as expected. The dashboard presents complex technical metrics in an easy-to-understand visual format, enabling both technical and non-technical team members to make informed decisions about their deployment. By providing these monitoring capabilities, the service helps maintain optimal performance and reliability of your model within the test environment while reducing the time needed to identify and resolve potential issues. The deployment of an AI model for testing purposes could also be done by Gradiant on another service (see related services) and then be monitored through this service.

Performance evaluation

Optimised Irrigation Scheduling for Smart Agriculture

This experimental service offers agrotech companies customised irrigation schedules based on real-time and historical data analysis. By taking into account soil moisture levels, weather conditions, and crop requirements, the service generates irrigation plans that improve efficiency and crop yields. The service delivers detailed reports with actionable insights, ensuring that water is used efficiently without compromising crop health.

Data analysis

People training services

Politecnico di Milano (POLIMI)

Università degli Studi di Milano (UMIL)

The AgrifoodTEF consortium comprises a set of testing and experimentation facilities and targeted services aimed at supporting customers in the testing and validation of systems and devices based on advanced technologies. Given the technical complexity of these facilities and services and the associated complexity of many activities making use of them, many customers can benefit from a preliminary training activity. The goal of such training is to make the best use of the facilities and services and fully exploit the allotted time when actual testing/experimentation occurs.

Customer training – involving both technical and procedural aspects – can either be sought by the customer autonomously leveraging the documentation provided by agrifoodTEF or directly supported by agrifoodTEF via this service; a combination of both approaches is also possible.

This service does not limit its applicability to customers that already know what they require from agrifoodTEF. The service also provides an opportunity for customers to discuss their specific needs and requirements with our team and be directed towards the set of services in the catalogue that best suit their needs. Especially where the customer envisages an articulate testing campaign, exploring their idea together with agrifoodTEF via this service can be very beneficial in planning subsequent activities.

This service can, if needed, be integrated with services S00109 (desk assessment activities for physical systems) or S00179 (desk assessment activities for digital systems or data) to anchor its operation, and possible further testing activities, more closely to the specific features of the system(s) developed by the customer.

People training

Performance and sustainability testing of robotic or AI-driven solutions

Lukasiewicz Poznanski Instytut Technologiczny (L-PIT)

This service provides a comprehensive assessment of the performance of systems, focusing on sustainability indicators such as energy consumption and input usage per hectare. It evaluates the impact of artificial intelligence (AI) or robotic solutions on the performance and sustainability of the associated equipment. The service includes tests such as functional testing, accelerated durability testing, and assessments of the system’s resilience to environmental factors and electromagnetic interference (EMI). By identifying potential design flaws or manufacturing defects, the service supports the development and optimisation of tested solutions, contributing to sustainable technological advancements.

Collection of test data

Data analysis

Desk assessment

Performance evaluation

Test design

Test execution

Test setup