Catalogue of Services

Data analysis

Data augmentation

Assessment of agricultural equipment connectivity through private 5G/NB-IoT network deployment

Gradiant Technologies

The service provides a comprehensive assessment of the connectivity of agricultural equipment, such as sensors and actuators, through private networks. By offering in-depth visibility into the network’s operation, it enables accurate troubleshooting, ensuring optimal connectivity for these kinds of devices. The service can handle the connection of multiple devices, making it suitable for validating the development of custom connectivity modules or testing agricultural equipment with built-in commercial modules. This allows for early detection of connectivity issues, fine-tuning module and network configurations, and ensuring smooth equipment operation. By deploying private networks, it is possible to implement configurations that simulate adverse conditions in deployment, such as successive outages, excessive packet loss, remote access failures to access the hardware, and more; practices that would be unthinkable in public networks. Furthermore, although the preference is for the deployment of cellular networks like 5G, 4G, or 3G, other communication technologies could be supported, like NB-IoT.

Desk assessment

Test design

Fondazione Bruno Kessler (FBK)

Test setup

Assessment of AI performance and software optimisation on edge computing devices

The tests in this service are aimed at products that run complex apps (based on a multitude of microservices, including heavy AI algorithms) in the fields, with limited to no connectivity to the cloud. In these cases, we propose a benchmarking designed to understand what the bottlenecks are in the implemented solution architecture to help TEF customers improve the efficiency and reliability of such products/solutions. The service may also encompass virtual facility elements (enriched datasets, virtual machines hosting containerised modules) blended with physical sensors and infrastructure in our test field or directly installed at the client's premises if need be for the duration of the experimentation.

Test design

Research Institutes of Sweden (RISE)

Test setup

Assessment of conformity roadmap for an innovation

Are you ready to launch your innovative product with complete confidence in its compliance and market success? Our service offers a thorough assessment of all necessary steps to ensure your product meets the latest standards, laws, and directives. We will guide you through the regulatory landscape, identifying the specific rules applicable to your product and providing tailored advice on how to adapt your innovation to meet these requirements.

Unsure if current laws cover your groundbreaking idea? We will also investigate whether new regulations are needed to support your innovation's potential.

In addition to compliance, we provide a comprehensive range of supplementary services that AgrifoodTEF can offer to further accelerate your success. From testing and validation to expert guidance on market strategy, we are here to support you every step of the way.

Università degli Studi di Napoli Federico II

Desk assessment

ELSA assessment

Blockchain in agriculture traceability systems

The service focuses on testing and developing blockchain technologies to enhance traceability in the agri-food sector. By integrating blockchain with IoT devices, the service ensures a transparent and immutable record of transactions across the entire supply chain. This enables agri-food companies to improve transparency, accountability, and trust while ensuring the integrity and safety of food products. Additionally, it drives efficiency and sustainability throughout the supply chain.

AI model training

Business modelling

Cybersecurity

Data analysis

Laboratoire National de Métrologie et d’Essais - LNE

Certification of AI processes by LNE

Today’s AI suppliers and AI users need to stand out from the competition, guarantee the performance of their algorithms or systems, and prepare to comply with the future European "AI Act" regulation, particularly with the obligation of transparency. With its dual expertise in certification and evaluation of AI algorithms, LNE has created a voluntary certification that meets this need. The aim of this certification standard is to define common requirements for the design, development, evaluation, and maintenance processes of all types of AI functionalities based on machine learning. It therefore covers all business sectors in which AI is used in order to guarantee the application of best practices that promote trustworthiness in AI. Website: https://www.lne.fr/en/service/certification/certification-processes-ai Webinar: https://www.lne.fr/fr/webinars/matinee-information-ia-act-certification-ia-l Technical documentation (FR): https://www.lne.fr/sites/default/files/bloc-telecharger/FTC-ISO-42001-LNE.pdf

Laboratoire National de Métrologie et d’Essais - LNE

Desk assessment

LCA assessment

Certification of Artificial Intelligence Management System (AIMS) of ISO/IEC 42001

The ISO/IEC 42001 is a global standard that provides a robust framework and structure within which AI systems can be developed, deployed and used responsibly. It sets out requirements and recommendations for establishing, implementing, maintaining and continuously improving an AI management system within the context of an organisation. Key controls included in the standard are risk management, AI impact assessment, system lifecycle management, performance optimisation, and supplier management. Its aim is to help organisations: Develop or use AI responsibly, Meet applicable regulatory requirements, and * Meet stakeholders' obligations and expectations. In this way, it provides concrete support to companies in optimising the use of AI by guaranteeing a level of control and confidence in the systems developed. Customers concerned: consulting firms; solution or application developers; integrators; companies integrating AI solutions purchased on the market or developed in-house into your offerings; competent authorities (decision-makers, regulators). Webinar: https://www.lne.fr/fr/webinars/iso-42001-certification-ia-lne-s Technical documentation (FR): https://www.lne.fr/sites/default/files/bloc-telecharger/FTC-ISO-42001-LNE.pdf

Certification

Conformity assessment

Desk assessment

LCA assessment

Cold chain monitoring

Università degli Studi di Napoli Federico II

The service focuses on developing and testing devices that ensure proper temperature conditions during the conservation and transportation of agri-food products. These devices capture and store temperature data throughout the process, offering reliable validation of temperature control. Additionally, the service provides advanced data analysis and reporting to help farmers and agri-food operators assess compliance, identify areas for improvement, and meet regulatory requirements.

Business modelling

Data analysis

Collection of datasets of images and sensor data using ground robots

Gradiant Technologies

The possibility to autonomously gather images and generic sensor data means a quantum leap in the creation of multispectral datasets in an automated and efficient way. A ground robot deployment occurs based on agreed requirements such as study area, measurement granularity, and data type so that it automatically captures all the information needed to generate a rich dataset. This not only makes data collection easy but also reduces human interference, hence reducing the chances of error, thus making the information obtained more valid. After the operation, the data is processed and tidied up to enable further analysis. In that respect, it can deliver faster and more accurate results for the customer, well-treated datasets according to each customer's needs, and at standards that can be further used in artificial intelligence models. With such information, customers will be better informed during the optimisation of their operations by using advanced technologies.

Politecnico di Milano (POLIMI)

Provision of datasets

Collection of test data during physical testing

Through this service, we provide specialised technical systems (both hardware and software) and expertise for collecting relevant data during testing.

The scope of the data collected can vary, encompassing information generated by the agrifoodTEF technical infrastructure (e.g., through the installation of IoT sensors), data from the system under test, and ground truth data for system evaluation. If needed, this service can facilitate the interconnection between agrifoodTEF's data collection systems and the customer's systems.

In addition to the raw data collected, we will supply customers with comprehensive documentation detailing the logged features, conditions of the testing environment at the time of testing, and any parameter values, variation ranges, and specifics necessary for reproducibility.

When required, data collection and documentation activities may involve field experts such as engineers and agronomists.

Please note that annotation and labeling of collected data, if necessary, can be provided through service S00115.

Politecnico di Milano (POLIMI)

Collection of test data during digital testing

One of the key activities during digital testing is the collection of data concerning the progression and the final outcome of the tests. Such data enable the evaluation of system performance by the customer or – if needed – by AgrifoodTEF (via Service S00184). This service manages the collection of data relevant to performance evaluation produced during the tests by both the system under test and the computational environment where the tests take place.

Examples of collected data comprise information produced within a virtual environment to simulate sensor data collection in a physical environment; statistics about AI model performance in the test and deployment phase (e.g., occupied memory, number of trainable parameters, training/optimisation loss, etc.); specific labels and annotations to use as ground truth for evaluating the system; and system output when subjected to a range of test conditions. The minimum set of data to be collected is defined by the evaluation metrics that the user chose (either on their own or with AgrifoodTEF support, via Service S00178) to process them; generally, a larger set of data wrt the minimum is selected by AgrifoodTEF together with the customer to provide a richer view of the system’s performance and to enable the application of other metrics in the future, if needed. As an output of the service, in addition to the raw data, we also provide the customer with documentation describing logged features and conditions of the testing environment at the time of testing, as well as any parameter values, variation ranges and specifics required for reproducibility purposes.