Catalogue of Services

Performance testing of deep learning solutions

University of Cordoba (UCO)

Location

Remote

Spain

Arable farming

Food processing

Greenhouse

Horticulture

Livestock farming

Tree Crops

Viticulture

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.

Performance Testing of Robotic Systems and AI Solutions

Lukasiewicz Poznanski Instytut Technologiczny (L-PIT)

Location

At user's premises

Poland

Arable farming

Food processing

Greenhouse

Horticulture

Livestock farming

Tree Crops

Viticulture

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.

Physical testing and data collection of agricultural sensors

University of Cordoba (UCO)

Location

Spain

Arable farming

Horticulture

Tree Crops

Viticulture

This service provides comprehensive data collection from various agricultural sensors used in farming environments. By ensuring high-quality data acquisition, this service supports testing, validation, and performance assessment of agricultural models, technologies, and equipment. The collected data can be used to evaluate sensor reliability, accuracy, and operational effectiveness, helping identify potential improvements. Through field testing and controlled experiments, customers gain valuable insights into their sensors' performance.

Physical Testing of Endurance & Reliability

Lukasiewicz Poznanski Instytut Technologiczny (L-PIT)

Location

At user's premises

Poland

Arable farming

Food processing

Greenhouse

Horticulture

Livestock farming

Tree Crops

Viticulture

We provide physical testing services, both in laboratory and field environments, to evaluate the endurance and reliability of devices and their components. These tests focus on the ability of components to withstand external conditions while maintaining proper functionality. Our testing process helps identify potential weaknesses in device design, offering insights that can inform adjustments to algorithms or hardware. The service is designed to ensure your systems can perform reliably under a variety of conditions, helping to prevent failures and maintain optimal performance in real-world applications.

Policy lab for innovative drone technologies in regulatory sandboxes

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

Location

Belgium

Arable farming

Greenhouse

Horticulture

Livestock farming

Tree Crops

Viticulture

This service is designed to support agri-food companies that want to test and evaluate the use of innovative drone technologies in a controlled environment—a so-called regulatory sandbox. Setting up such a drone sandbox requires navigating complex airspace regulations, flight safety rules, environmental constraints (e.g., drift, pesticide use), and data protection standards. Our experts help you design and prepare a sandbox operation from A to Z: from selecting a test location and identifying legal permissions needed to preparing risk assessments, notifying authorities, and ensuring safe operational procedures. We also offer advice on sensor integration, traceability, and aligning with EU drone and agricultural regulations such as Regulation EU 2019/947 on the operation of unmanned aircraft. This is an ideal service for companies wishing to run trials with crop spraying or precision seeding drones before launching commercially. - Set up a regulatory sandbox to safely and legally test spraying or seeding drones. - Understand the full list of permissions, safety protocols, and risk assessments required. - Learn how to engage with authorities (e.g., airspace regulators, pesticide control bodies). - Get support in preparing the documentation to operate drones legally during the test phase.

Preparation of computational test environment

Politecnico di Milano (POLIMI)

Università degli Studi di Milano (UMIL)

Location

Italy

Remote

Arable farming

Food processing

Greenhouse

Horticulture

Livestock farming

Tree Crops

Viticulture

To successfully conduct a digital testing campaign, preparatory activities are usually required to set up the computational environment used for testing. This service performs activities such as:

• setting up the hardware resources needed to run the tests

• configuring and initialising the virtual environments used for testing (e.g., Docker images, private “data rooms” for safe data sharing)

• installation and configuration of required software packages and dependencies 

• setting up authentication layers, user roles and credentials as needed

• migration and/or exchange of required data and ground truth annotations 

• installation and configuration of a simulator

• importing and configuring a previously defined simulated environment

• executing dry-runs to check that all elements of the test environment operate as required

Environment preparation is done according to an environment design provided by the customer. If needed, such a design can be done by agrifoodTEF for the customer via Service S00176.

Interested customers can get support from agrifoodTEF for the entire pipeline involved in digital testing, from the design of its other elements beside the environment (namely, the testing protocol via service S00177 and the evaluation metrics via service S00178) to test execution (service S00182), data collection (service S00183), and evaluation (S00184). Support in interconnecting the systems under test to the digital testing environment, if needed, is available via service S00181.

Preparation of physical test environment

Politecnico di Milano (POLIMI)

Università degli Studi di Milano (UMIL)

Location

Italy

Arable farming

Greenhouse

Horticulture

Tree Crops

Viticulture

Testing a system in a physical environment requires an environment that has been prepared and set up to support the specific testing activities to be executed. This service supports the customer in the execution of the tests by taking care of such preparation and setup.

The service involves:

• agronomic preparation: e.g., providing the required soil conditions, making sure that a given in-field configuration of specific plants at a given growth stage is available at the time of testing, and so on;

• technical preparation: e.g., setting up infrastructure elements such as RTK GPS base stations, environmental sensors, power and networking, as well as the computational environment required to interface with the system under test to acquire and possibly pre-process experimental data.

If AgrifoodTEF facilities are involved in the experimental activities, the service also includes configuring the testing facility accordingly.

Environment preparation is done according to an environment design provided by the customer. If needed, such a design can be done by AgrifoodTEF for the customer via Service S00106.

Interested customers can get support from AgrifoodTEF for the entire pipeline involved in experimental testing, from the design of its other elements beside the environment (namely, the testing protocol via service S00107 and the evaluation metrics via service S00108) to test execution (service S00113), data collection (service S00113), and evaluation (S00114).

The output of Service S00110 is the environment ready for the execution of the testing campaign at the time requested by the customer.

Providing demonstration testing on the test field of robotic or AI-driven solutions

Lukasiewicz Poznanski Instytut Technologiczny (L-PIT)

Poznan Supercomputing and Networking Center (PSNC)

Location

Poland

Arable farming

Food processing

Greenhouse

Horticulture

Livestock farming

Tree Crops

Viticulture

As part of the service, a testbed will be provided for product demonstration of robotic machines along with AI systems. The polygon will be configured and prepared to meet the customer's individual needs in terms of field operations, technological processes, and communication. The service provider will provide polygons with a variety of surfaces according to the customer's specifications, e.g., meadow, ploughed field. The service will include soil moisture control, continuous monitoring of the field, and all activities carried out on it. The service providers will provide the necessary assistance and infrastructure needed to support the fieldwork carried out by the surveyed sites. The service may also involve demonstration of AI systems used in apiaries.

Provision/generation of synthetic datasets for robust deep learning models in farming

Danish Technological Institute (DTI)

Location

Denmark

Arable farming

Food processing

Greenhouse

Horticulture

Livestock farming

Tree Crops

DTI’s synthetic dataset generation service enables agri-tech companies to develop more reliable and scalable deep learning (DL) models for farming applications. By simulating diverse farming scenarios, including rare or hard-to-capture conditions such as extreme weather, pest infestations, and various crop growth stages, DTI addresses critical challenges like data scarcity and class imbalance. Additionally, DTI augments real-world datasets by introducing artificial variations in lighting, angles, and occlusions, closely mimicking real-world environmental factors. This service empowers DL models to generalise better, handle edge cases effectively, and perform robustly under practical farming conditions, ultimately supporting agri-tech innovation and scalability.

Provision of a phenotyping greenhouse with extensive sensor set - NPEC

Wageningen University WUR

Location

Netherlands

Arable farming

Greenhouse

Horticulture

NPEC will enable you to make accurate, high-throughput studies of plant performance possible in relation to relevant biotic (diseases, bacteria) and abiotic (light quantity and quality, nutrients, temperature, moisture, soil pH and atmospheric CO2 level) factors across a range of scales. 

The experiments can be performed in a greenhouse compartment with an extensive set of sensors to measure the plant and it’s environment. In growth rooms smaller scale experiments can be performed, with a special chamber for studying the roots of plants using transparent growth media. In the open field crop measurements can be performed using the Traitseeker for RGB, spectral and 3D measurements. 

Provision of a virtual playground for robots

Wageningen University WUR

Location

Netherlands

Remote

Greenhouse

Horticulture

The service consists of a photorealistic digital greenhouse environment, built in a game engine. 

The greenhouse is configurable to different sizes, light conditions (sun, supplementary lighting) or row spacing. In the greenhouse different crops can be placed to either test AI detection networks or complete robotic systems, as there is a ROS2 integration available. The environment is made in a way that physical properties of the objects have influence on other components or robot interactions. 

Provision of cloud-based data storage

Lukasiewicz Poznanski Instytut Technologiczny (L-PIT)

Poznan Supercomputing and Networking Center (PSNC)

Location

Remote

Arable farming

Food processing

Greenhouse

Horticulture

Livestock farming

Tree Crops

Viticulture

Within the service we provide: - remote access to the disc space, allowing for safe storage of data (e.g., from sensors, photos from specialised cameras, weather data, satellite data, archive historical crop yields, etc.). - Depending on the needs, the available disc space can range from several gigabytes to tens, if not hundreds, of terabytes. - The stored data may be used directly by the customer or may be used in other services (this refers to using data for AI/ML model training, testing and validation for applications such as precision farming, yield prediction and pest detection). - The ability to store both structured (e.g., CSV, SQL) and unstructured data (e.g., images, videos)