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Collection of datasets of images and sensor data using UAV

The service offers automated drone deployments to efficiently gather images and sensor data, creating comprehensive multi-spectral datasets. The drones operate based on agreed requirements, such as the study area, data type, and measurement granularity, ensuring all necessary information is captured with precision. This makes data collection easy and reduces human interference, hence reducing the risk of error and making the obtained information more valid. After flying, the data is processed and tied up to enable further analyses. The service can deliver fast, comprehensive, and well-treated datasets according to each customer's needs using methods 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.

Collection of test data

Provision of datasets

Collection of test data during physical testing

Politecnico di Milano (POLIMI)

Università degli Studi di Milano (UMIL)

Through this service, we offer dedicated technical systems (hardware and software) and expertise for the collection of relevant data during testing.

The scope of data collected can range from data generated by the AgrifoodTEF technical infrastructure (e.g., through the installation of IoT sensors) to the collection of data generated by the system under test to the generation of ground truth for evaluating the system. If necessary, service S00111 may be leveraged to take care of the interconnection between AgrifoodTEF data collection systems and the customer’s systems.

In addition to the raw collected data, we will also provide customers with complete documentation describing the 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.

Where necessary, data collection and documentation activities can require the involvement of field experts (e.g., engineers, agronomists).

Note: annotation and labelling of collected data, if needed, can be provided via service S00115.

Collection of test data

Collection of test data during digital testing

Politecnico di Milano (POLIMI)

Università degli Studi di Milano (UMIL)

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.

Collection of test data

Combating forgery in the agri-food chain

Università degli Studi di Napoli (UNINA)

The service focuses on developing customised holographic technology to fight the counterfeiting of agricultural products. It provides a tailored security system using advanced holograms that strengthens product integrity, builds consumer trust, and supports the broader goal of safeguarding the agri-food sector from fraud.

Performance evaluation

Test execution

Compliance assessment for robotic machines

RISE - Research Institutes of Sweden

Ensure your company develops robotic machinery that complies with all relevant regulations, standards, and directives with the support of expert guidance. Beyond meeting existing requirements, expert assistance is provided in areas where the rules are unclear or undefined. For situations with ambiguous or missing guidelines, practical solutions are offered based on extensive experience in functional safety.

Autonomous robotic machinery often relies on complex sensor technologies, such as radar, LIDAR, and cameras, to detect nearby individuals. Demonstrating how these advanced sensors can be integrated into safety functions is challenging, as it involves considering not only traditional functional safety aspects but also emerging factors like AI and Safety of the Intended Functionality (SOTIF).

Support includes performing comprehensive risk assessments, identifying relevant safety functions, and establishing high-level safety requirements for each. Depending on the project's focus, further guidance can be provided to ensure the selected safety concept aligns with applicable functional safety standards.

Desk assessment

ELSA assessment

Conformity assessment and compliance tests

Lukasiewicz Poznanski Instytut Technologiczny (L-PIT)

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

Performance evaluation

Test design

Test execution

Test setup

Crop Dataset Generation

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

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.

Collection of test data

Provision of datasets

Data Collection using UGVs and modular sensor payloads

Fondazione Bruno Kessler (FBK)

This service consists of deploying a modular sensor payload (equipped with Lidar, inertial motion units, GNSS, Radar, stereo cameras, and ultra-wideband technology) to agricultural environments such as vineyards and orchards to collect and annotate high-fidelity datasets tailored to customer needs. Data are systematically labelled and can be delivered to agrifoodTEF customers as structured datasets for AI-driven agricultural solutions.

Collection of test data

Data labelling

Politecnico di Milano (POLIMI)

Università degli Studi di Milano (UMIL)

This service concerns the curation of ground truth annotations for testing and experimentation data. More specifically, data labelling concerns the association of portions of data (e.g., image regions) to descriptions that provide information about the significance of those portions. In particular, labelling is required whenever the data are meant to be used for model training.

We also provide labelling services for domain-specific data attributes, such as disease indicators and their confounding factors, by experts in agronomy. The labelled data will be accompanied by a report synthesising quality metrics and statistics like the number of data points annotated, percentage of the full set covered for the annotations, number of annotators and the inter-annotator agreement.

Data augmentation

Data validation and processing

Politecnico di Milano (POLIMI)

Università degli Studi di Milano (UMIL)

Through this service, we offerers the ability to validate and augment the data collected during the different testing phases. Examples of operations that this service can provide include quality checks (e.g., presence of all datastreams, presence of drop-outs, technical quality such as focus or exposure of camera images, agronomic significance of data); preparation for performance evaluation (e.g., identification of the images containing selected visual markers or specific plants, association between images from the system under test and the ground truth); data processing (e.g., transcoding); and augmentation (e.g., generation and incorporation of metadata). Data labelling (e.g., to enable training of AI models) is not included as it is the object of a separate service (S00291).

Data analysis

Data augmentation

Design of computational environments for digital testing

Politecnico di Milano (POLIMI)

Test activities involve three key components: the environment (where the tests are conducted), the protocol (defining what tests are performed and how), and evaluation metrics (used to assess the test results). Through this service, we assist customers in designing a computational environment tailored to running the digital tests required for their software components. Specifically, this service:

Defines the data (and metadata) requirements needed to support the test
Identifies the necessary software environment (e.g., operating system, libraries)
Selects an appropriate simulator and defines simulated environments, if required
Chooses remote access tools, if applicable.

As a result, we deliver a comprehensive design of the testing environment, including specifications such as memory requirements, GPU/TPU acceleration, and necessary libraries. This environment can be deployed on either the agrifoodTEF digital infrastructure or the customer's computing infrastructure (i.e., on premises).

Test design

Design of evaluation metrics for digital testing

Politecnico di Milano (POLIMI)

Università degli Studi di Milano (UMIL)

Any test activity involves three main components, i.e.: environment (where the tests take place), protocol (defining what activities are executed and how) and evaluation metrics (used to assess the results of the tests). This service concerns the last element; its goal is to design the best metrics to evaluate the performance of digital systems such as (for instance) AI models or Computer Vision software. The digital environment and the testing protocol metrics can be designed -if required- via services S00176 and S00177.

Our team will identify and define with customers the most adequate set of quantitative metrics to assess the outcome of the digital testing activities. In order to ensure the relevance of the metrics with respect to the real-world use cases, the team will involve engineers and agronomists. 

The metrics will be adapted not only to the task that the digital system under test (e.g., a piece of software) is designed to perform, but also to the features of the data used for the tests. For instance, a customer that has developed a machine incorporating an AI model will be interested in testing the model on data generated by their own machine: the performance metrics will therefore need to be adapted to the specific features of those data.

Test design

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