Catalogue of Services

Lukasiewicz - Poznanski Instytut Technologiczny

Test design

Test execution

Test setup

Preparation of computational test environment

Politecnico di Milano

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 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) and 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.

Test setup

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

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.

People training

Performance evaluation

Laboratoire National de Métrologie et d’Essais - LNE

Test design

Test execution

Test setup

Provision of dataset for agricultural applications

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.

Fondazione Bruno Kessler

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.

Politecnico di Milano

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., variety of environmental conditions represented in a sensor stream); lower-level features to be defined concern formatting and packaging of the data.

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 for simulation and visualisation

Wageningen Research - WR

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.

National Institute for Research in Digital Science and Technology  - INRIA

Provision of general-purpose datasets via multisensory ground robot

General-purpose datasets serve two primary objectives: (i) evaluating mobility algorithms and (ii) developing and assessing general-purpose AI applications. In the context of mobility algorithms, this pertains to classical robotics tasks such as mapping, localisation, SLAM (Simultaneous Localisation and Mapping), and navigation. Meanwhile, general-purpose AI applications focus on advancing algorithms and feeding decision support systems (DSS) for tasks such as, but not limited to, weed detection, health monitoring, growth and maturity assessment, and yield estimation in areas like arable farming, horticulture, food processing, forestry, and tree management. A significant challenge in developing AI solutions for agricultural robotics lies in the dynamic nature of agricultural environments, which fluctuate with different seasons and weather conditions. To address this, acquiring consistent and periodic data is essential for monitoring these changes effectively. This real-time data collection, often facilitated by ground robots, is crucial for developing efficient algorithms and AI solutions. Such datasets can support the development of sensor-specific techniques or be leveraged to create multisensory algorithms, enabling more accurate and adaptable systems for agricultural applications.

National Institute for Research in Digital Science and Technology  - INRIA

Provision of general-purpose datasets via a multisensory aerial robot.

We provide general-purpose datasets that can be used by customers to evaluate mobility algorithms and to develop and assess general-purpose AI applications. In the context of mobility algorithms, this pertains to classical robotics tasks such as mapping, localisation, and SLAM (Simultaneous Localisation and Mapping). Meanwhile, general-purpose AI applications focus on advancing algorithms and feeding decision support systems (DSS) for tasks including, but not limited to, weed detection, health monitoring, growth and maturity assessment, and yield estimation in areas such as arable farming, horticulture, food processing, forestry, and tree management. A significant challenge in developing AI solutions for agricultural robotics lies in the dynamic nature of agricultural environments, which fluctuate with different seasons and weather conditions. To address this, acquiring consistent and periodic data is essential for effectively monitoring these changes. This real-time data collection, often facilitated by aerial robots, is crucial for developing efficient algorithms and AI solutions. Such datasets can support customers in the development of sensor-specific techniques or be leveraged to create multisensory algorithms, enabling more accurate and adaptable systems for agricultural applications.

National Institute for Research in Digital Science and Technology  - INRIA

Provision of general-purpose datasets with user-specified sensor(s)

General-purpose datasets serve two primary objectives: (i) evaluating mobility algorithms and (ii) developing and assessing general-purpose AI applications. In the context of mobility algorithms, this includes classical robotics tasks such as mapping, localisation, SLAM (Simultaneous Localisation and Mapping), and navigation. Meanwhile, general-purpose AI applications focus on advancing algorithms and supporting decision support systems (DSS) for tasks such as, but not limited to, weed detection, health monitoring, growth and maturity assessment, and yield estimation in areas like arable farming, horticulture, food processing, forestry, and tree management. A significant challenge in developing AI solutions for agricultural robotics lies in the dynamic nature of agricultural environments, which fluctuate with different seasons and weather conditions. To address this, acquiring consistent and periodic data is essential for effectively monitoring these changes. This real-time data collection, often facilitated by aerial and/or ground robots equipped with user-specified sensors, is crucial for developing efficient algorithms and AI solutions. Such datasets can support the development of sensor-specific techniques or be leveraged to create multisensory algorithms, enabling more accurate and adaptable systems for agricultural applications.

Provision of raw image datasets in field crops

Wageningen Research - WR

In order to develop AI applications or vision functionalities in robots, one of the first steps is to have high-quality images of crops, weeds, nutrient deficiencies, diseases, or pests available. As the phenotype of plants can really differ between regions and seasons, providing balanced datasets with high-quality images and different illumination could save valuable (development and acquisition) time. The data can be used for annotation purposes or to have a first visual check if an existing trained algorithm works sufficiently.

Collection of test data