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Scanning as a Service for synthetic data generation and modelling

Wageningen University WUR

We offer a comprehensive 3D scanning and model preparation service tailored for agriculture and food applications, built on expertise developed through scanning diverse objects such as rapeseed plants, tomato seedlings, and fish. This service transforms real-world items into accurate, high-resolution 3D models, ready for use in synthetic data generation, robotics simulations, and plant phenotyping. Using tested scanning approaches—including photogrammetry with stationary or rotating cameras, DSLR, and smartphone captures—we can reliably acquire detailed image sets. Using these 3D models, your AI detection algorithms can be tested on high-quality 3D data. The data can be used to test detection algorithms, or it can be used in complete virtual environments to test robotic applications. Beyond static models, dynamic functionality can be added by assigning physics properties and enabling randomisation—altering geometry, texture, and patterns to expand dataset diversity. This allows models to be seamlessly integrated into simulation environments for testing AI, testing robotic systems, or conducting virtual experiments to test and improve the systems. Whether for research, product development, or automation, our solution delivers a scalable, future-ready platform for realistic and adaptable 3D tests.

Collection of test data

Provision of datasets

Setup for AI solution testing

Fondazione Bruno Kessler (FBK)

Digital testing of an AI solution often requires action to obtain the proper configuration. This service contains all the required actions necessary to configure, integrate and execute the client AI solution with FBK test environment. Carrying out these preliminary activities, FBK will be able to have a working setup that will be the entry point for subsequent testing and validation services.

Test setup

Support in interconnecting systems to AgrifoodTEF’s computational test infrastructure

Politecnico di Milano (POLIMI)

Università degli Studi di Milano (UMIL)

Digital testing requires interaction among several different components which have to reliably cooperate to enable the testing activities and to ensure that their outcomes reliably reflect the system’s capabilities. When digital testing of customer systems leverages agrifoodTEF’s computational infrastructure, integration between the infrastructure and the systems to be tested is needed before the tests/experiments can be executed. If the customer does not possess the technical expertise – or does not want to devote resources – to perform the integration, this service can support them in the process. Help is provided by a team of expert engineers.

This service concerns the integration of the customers’ system and/or data used for testing with the agrifoodTEF digital testing infrastructure. In particular, integration is crucial to ensure smooth and uninterrupted communication between the system and the infrastructure that produces, collects and/or processes test data.

This service also includes the development of any custom components needed to ensure a two-way integration between the system and the reference infrastructure (e.g., software components to format data or to perform transcoding).

Test execution

Test setup

Technology advancement and readiness assessment

Lukasiewicz Poznanski Instytut Technologiczny (L-PIT)

Poznan Supercomputing and Networking Center (PSNC)

Wielkopolska Agricultural Advisory Center in Poznań (WODR)

This service helps you understand where your AI-powered product, system, or software stands in terms of technological maturity. We conduct a basic, yet structured evaluation of your solution—including physical testing of selected components and general system behaviour—to identify its current development stage and key areas that need improvement. We also assess the quality of documentation, system architecture, and software implementation. For example, when evaluating an apple-picking robot, we assess the readiness and integration of its core subsystems: the gripper mechanism, robotic arm kinematics and control, and the vision system responsible for detecting ripe fruit. We analyse areas where current solutions can be improved or require some redesign and whether the robot as a whole can effectively perform “scene ”cleaning”—removing ripe apples while preserving unripe ones and the plant structure. Based on our findings, we prepare a clear roadmap and a development plan with recommendations for the next steps, which may include further testing or refinement through other agrifoodTEF services. As part of the process, we also confirm the current Technology Readiness Level (TRL) of each major component. The outcome helps you focus your R&D work and understand what is still required to move your solution closer to full deployment and commercial readiness.

Conformity assessment

Test and validate intelligent solutions based on sensors to optimise livestock farming

Centre d'Estudis Porcins (CEP)

Universitat de Lleida (UdL)

The service validates that the tested solution performs as expected by the vendor or manufacturer. The objective is to detect potential mistakes in the sensor readings, the processing of the signal gathered by the sensor or in the intelligent component deriving the final information to be provided to the user to optimise livestock farming . The test can be performed at sensor or application level. The tests and validation can be performed in a laboratory or in an experimental pig farm equipped with automatic and manual measures of different variables (feed, water, CO2, NH3, temperature, ventilation, liveweight, etc.)

Collection of test data

Testing and evaluating sensor devices

National Institute for Research in Digital Science and Technology  (INRIA)

The SOPHIA infrastructure will offer the possibility to test and evaluate the performance of a sensor device in a locally controlled environment. While our indoor facility provides a fully controlled environment (light, volume), all facilities in SOPHIA can be customised to test and evaluate the sensor device. The service consists of three main steps. First, the sensor device will be integrated into the facility. After that, the facility will be customised according to the final application of the sensor device. Then, the performance of the sensor device will be evaluated using quantitative and qualitative metrics. Comparison could be proposed as a complementary option to position the performance of the sensor device in relation to the current state of the technology available in SOPHIA.

Collection of test data

Data analysis

Desk assessment

People training

Performance evaluation

Provision of datasets

Test design

Test execution

Test setup

Testing and evaluating the robot’s functionalities

National Institute for Research in Digital Science and Technology  (INRIA)

The SOPHIA infrastructure will offer the possibility to test and evaluate the performance of the robot’s functionalities in a locally controlled environment. While our indoor facility will offer a fully controlled environment (light, volume), all facilities at SOPHIA can be customised to test and evaluate the robot’s functionalities. The service consists of three main steps. The robot will be integrated into the facility. After that, the facility will be customised according to the functionality of the robot. Then, the performance of the robot’s functionalities will be tested and evaluated with quantitative and qualitative metrics.

AI model training

Collection of test data

Data analysis

Desk assessment

People training

Performance evaluation

Test design

Test execution

Test setup

Testing and evaluation of mobility algorithms with ground robots

National Institute for Research in Digital Science and Technology  (INRIA)

The SOPHIA infrastructure provides the ability to test and evaluate mobility algorithms embedded on a ground robot. Mobility algorithms concern the classical robotics functionalities of mapping, localisation, SLAM, and navigation. The ground robot is equipped with an array of sensors, including a camera, LiDAR, IMU, and RTK-GPS for ground truth evaluation. The service proceeds in three stages. Firstly, we evaluate the algorithm using representative datasets. After that, the algorithm is integrated into a ROS2 architecture and evaluated with the local agrifoodTEF test infrastructure (various areas are possible). The performance of different attributes of the algorithm is assessed using quantitative and qualitative metrics. Benchmarking could be proposed as a complementary option to position the performance of the proposed algorithm in relation to the current state of the art. The final step involves field testing under real conditions at a specific end-user or customer site using the mobile living lab, which consists of a mobile laboratory deployed in the field and connected to the real robot for monitoring and evaluation purposes.

Collection of test data

Data analysis

Desk assessment

Performance evaluation

Test design

Test execution

Test setup

Testing and evaluation of mobility algorithms with aerial robots

National Institute for Research in Digital Science and Technology  (INRIA)

The SOPHIA infrastructure will offer the possibility to test and evaluate the mobility algorithms embedded on an aerial robot. Mobility algorithms concern the classical robotics functionalities of mapping, localisation, SLAM, and navigation. The aerial robot is equipped with an array of sensors, including a camera, LiDAR, IMU, and RTK-GPS (for ground truth evaluation). The service consists of three main steps. To begin with, the algorithm is evaluated using representative datasets. After that, the algorithm is integrated into a ROS2 architecture and evaluated with the local agrifoodTEF test infrastructure (various areas are possible). The performance of different attributes of the algorithm is evaluated using quantitative and qualitative metrics. Benchmarking could be proposed as a complementary option to position the performance of the proposed algorithm in relation to the current state of the art. The final step involves field testing under real conditions at a specific end-user or customer site using the mobile living lab (which consists of a mobile laboratory deployed in the field and connected to the real robot for monitoring and evaluation purposes).

Collection of test data

Testing and validating AI models in a simulated environment

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

This service enables the thorough testing and validation of your AI models within a simulation environment, ensuring optimal performance before real-world deployment. We implement your AI model and its corresponding dataset into advanced simulation software, allowing it to be tested without hardware limitations or with hardware-in-the-loop simulations where AI is (partly) integrated into the hardware or operating software. For example, the AI could simulate a robotic system navigating a field to detect and remove weeds. This method ensures your model functions accurately and efficiently under various conditions, accelerating its readiness for practical use in agri-food applications like precision farming or automated harvesting.

AI model training

Collection of test data

Performance evaluation

Test execution

Testing for safety in alignment with relevant legislation

Fondazione Bruno Kessler (FBK)

The purpose of this service is to provide advanced testing for AI and navigation software in the agrifood sector. By leveraging AI-powered test case generation, the service aims to create enriched and realistic testing scenarios that address safety and performance challenges in the control systems of complex agricultural systems. It seeks to support companies in identifying performance issues, allowing them to optimise system efficiency and reliability, with the objective of reducing costs and improving overall system performance.

Performance evaluation

Test execution

Test setup

Testing of AI-based sensor performance

Josephinum Research (JR)

Our service thoroughly evaluates AI-based sensors by measuring key metrics like accuracy, mean squared error, and other tailored parameters. Testing is conducted in real-world environments where the sensor will be used, with reference data recorded or labelled for analysis. For certain sensors, we also utilise benchmark datasets to validate performance. We assess consistency under identical conditions, adaptability to environmental changes (e.g., light, temperature, or weather), and improvements in accuracy over time. Additionally, we measure power consumption, processing efficiency, and memory usage to ensure optimal resource utilisation. Response time between detection and action is evaluated for real-time applications, while extreme condition testing (e.g., bright light, rainy conditions, compacted soil) ensures robustness in challenging environments. We also verify data security and resilience against adversarial attacks. This comprehensive testing is essential, ensuring sensors meet high standards of precision, reliability, and efficiency. Benchmark datasets, standardised data collections used for validation, provide a reliable baseline for performance comparison. By addressing these aspects, our service ensures your sensor technology is ready for real-world deployment, delivering the performance and adaptability your application demands.

Collection of test data

Performance evaluation

Test design

Test execution

Test setup

Catalogue of Services

Our infrastructures serve various sectors and countries!