Catalogue of Services

Desk assessment

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.

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.

Lukasiewicz - Poznanski Instytut Technologiczny

People training services

Politecnico di Milano

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 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

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.

Desk assessment

Performance Data Collection for Agricultural Technologies

This experimental service involves monitoring and collecting performance data from newly implemented technologies in agricultural production environments. Data is collected in real-time or at regular intervals, capturing key metrics such as water or energy usage, production yield, and overall efficiency. This data helps in evaluating the system’s performance and provides valuable insights for further optimisation and decision-making.

Performance Evaluation and Efficiency Measurement Experimenting with Different Agricultural Technologies

This service evaluates the performance of newly implemented agricultural technologies by analysing key performance metrics such as resource usage, production yield, and cost savings. The evaluation compares the technology's projected benefits against real-world performance data, identifying areas where the system is performing well and areas where improvements can be made. The goal is to provide a clear picture of the technology's effectiveness and its impact on the overall production process.

Laboratoire National de Métrologie et d’Essais - LNE

Performance evaluation of AI and robotics based on physical testing environments

By having your AI or robotic system tested by LNE, you ensure it meets the highest standards of safety and performance, boosting your product’s reliability and trustworthiness in the market. Partnering with LNE for rigorous testing of your robotic and AI technologies gives you a competitive edge, as our testing report enhances credibility and opens doors to new market opportunities both locally and globally. Testing of devices with AI-integrated control systems by our LE.IA facility focuses on both performance and performance-related safety. Our tests, conducted under controlled laboratory conditions, ensure optimal reproducibility and repeatability, providing reliable insights into the system's efficiency and safety-critical functionalities.

Performance evaluation of an AI model

Our service provides a thorough evaluation of AI models by utilising a wide range of benchmark datasets—from images and sensor data to remote sensing and multimodal data. This comprehensive testing assesses the model's performance across standard metrics, specifically chosen based on the model’s intended tasks. The results offer insights into the model’s effectiveness and accuracy, guiding developers in refining their AI solutions to achieve optimal performance in real-world applications.

Performance testing of deep learning solutions

University of Cordoba

This service evaluates the performance of deep learning algorithms to determine their efficiency and feasibility for migration to embedded devices. By leveraging high-performance computing (HPC) and GPU resources, this testing helps developers understand how well their algorithms perform in terms of speed, accuracy, and resource consumption. The service is particularly beneficial for applications in food processing, such as beekeeping and pollen counting, where real-time data processing is essential.

Lukasiewicz - Poznanski Instytut Technologiczny

Performance Testing of Robotic Systems and AI Solutions

We provide physical testing services, under both laboratory and field conditions, to assess the proper performance of the object under test, the level of quality, and the performance achieved. These tests focus on evaluating the performance of an object under laboratory and operational (field) conditions while maintaining its proper functionality. Our testing process helps identify potential weaknesses in the design of the device, offering insights that can inform adjustments to algorithms or hardware. This service aims to ensure that systems perform reliably under a variety of conditions, helping to prevent failures and maintain optimal performance in real-world applications.

Desk assessment