Catalogue of Services

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.

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)

Provision of dataset for agricultural applications

Laboratoire National de Meterologie et d'Essais (LNE)

Location

Remote

Arable farming

Food processing

Greenhouse

Horticulture

Livestock farming

Tree Crops

Viticulture

Our service offers a comprehensive collection of high-quality datasets tailored for agricultural applications, specifically designed to facilitate the testing and training of AI systems. These datasets encompass a wide range of data types, such as RGB or infrared images, 3D scans of LiDAR, or tabular data, ensuring that users have access to the relevant information needed for effective model development. By providing clean, well-structured, and annotated data, we empower agricultural businesses, researchers, and developers to enhance their AI solutions, driving innovation and efficiency in the industry. With our datasets, users can accelerate their projects, improve decision-making, and ultimately contribute to more sustainable agricultural practices.

Provision of datasets

Politecnico di Milano (POLIMI)

Università degli Studi di Milano (UMIL)

Location

Italy

Remote

Arable farming

Food processing

Greenhouse

Horticulture

Livestock farming

Tree Crops

Viticulture

During the development of systems based on AI and/or robotics, tailored data are needed to support the realisation, validation and optimisation of systems and subsystems. This service aims at providing such data.

AI models are a noteworthy example of systems that require data to be trained; more generally, any system which processes data produced via artificial perception (such as images, videos, LiDAR, ultrasound, radar...) requires suitable data to be built, tested and fine-tuned.

This service allows a customer to define, together with agrifoodTEF, the specific features of the data that the customer’s activities require and to receive datasets precisely matching such features. Among the features to be defined are the type, number and specifications of the data streams comprised in the datasets (e.g., when multi-sensor data are required) and the range of variations encompassed by the data (e.g., the variety of environmental conditions represented in a sensor stream); lower-level features to be defined concern the formatting and packaging of the data.

Provision of datasets

Josephinum Research (JR)

Location

Remote

Arable farming

Horticulture

Tree Crops

Viticulture

Our service offers a comprehensive dataset tailored for training or testing your solutions in the technology and agricultural sectors. Whether you're looking to enhance existing models or build new ones, our versatile data can be adapted to fit your specific needs. For those requiring an even more robust solution, our service seamlessly integrates with our data augmentation and labelling service (S00172), ensuring that you have access to well-organized and precisely labelled data. This flexibility allows us to support a variety of sensors and use cases, utilizing both existing data records and capturing new data in your application area. Optimize your agricultural technology solutions with our data services, designed to propel your projects forward efficiently and effectively.

Provision of datasets

Fondazione Bruno Kessler (FBK)

Location

Remote

Arable farming

Horticulture

Tree Crops

Viticulture

FBK is developing its own datasets based on its experience in the robotics and agricultural sector. These datasets can be used for training and testing of the solution proposed by the customer. Different kinds of datasets can be provided (raw, unprocessed, or processed data) according to the customer's specific needs. This service is often an essential first step for the validation process of a new product.

Provision of datasets for simulation and visualisation

Wageningen University WUR

Location

Netherlands

Remote

Arable farming

Horticulture

Tree Crops

In order to develop digital twins for agriculture, reliable long-term datasets on the agronomy and ecology of fields are crucial. From several long-term experiments and agricultural systems (like the Farm of the Future), datasets of fields are available with registrations of crop operations, crop measurements (e.g., timeseries of vegetation indices), yield, soil, & weather data, and data on key performance indicators. In some cases, other sensor data (tractor data, soil moisture, insect monitoring) are also available. New datasets can be developed based on specific needs.

Provision of datasets for validation of AI algorithms for livestock farming

Wageningen University WUR

Location

Netherlands

Remote

Livestock farming

Thanks to 30 years of data gathering at Agro-innovation Centre De Marke, multiple data sets are available. During these years, data is gathered on individual cow events, barn emissions, yields of specific and typical livestock farm crops, soil samples were taken, and feeding strategies were developed. Due to the systematic approach of De Marke, this data can be easily reused for setting up specific validation of AI algorithms. Additionally, new data can be gathered as requested. 

Satellite: Evaluation of results of physical testing

University of Cordoba (UCO)

Location

Remote

Spain

Arable farming

Horticulture

Livestock farming

This service offers an evaluation of satellite-derived data quality by applying performance metrics that assess model reliability and effectiveness. The service includes a comprehensive analysis of test data and previously collected ground truth data to validate the performance of models used in precision agriculture. With a detailed performance report, clients receive valuable insights that support model improvement and informed decision-making in agricultural management.

Scanning as a Service for synthetic data generation and modelling

Wageningen University WUR

Location

Netherlands

Remote

Arable farming

Food processing

Greenhouse

Horticulture

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.

Setup for AI solution testing

Fondazione Bruno Kessler (FBK)

Location

Remote

Arable farming

Food processing

Greenhouse

Horticulture

Livestock farming

Tree Crops

Viticulture

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.