Catalogue of Services

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. 

Provision of general-purpose datasets via multisensory ground robot

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

Location

At user's premises

France

Arable farming

Food processing

Greenhouse

Horticulture

Tree Crops

Viticulture

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.

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

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

Location

At user's premises

France

Arable farming

Food processing

Greenhouse

Horticulture

Tree Crops

Viticulture

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.

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

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

Location

At user's premises

France

Arable farming

Food processing

Greenhouse

Horticulture

Tree Crops

Viticulture

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 real-time data streams

Lukasiewicz Poznanski Instytut Technologiczny (L-PIT)

Location

At user's premises

Poland

Arable farming

Food processing

Greenhouse

Horticulture

Livestock farming

Tree Crops

Viticulture

The service provides access to data streams collected from equipment tested by other services or using specially installed sensor or camera arrays in specific test environments. It includes the provision of data streams from sensors and cameras installed to meet specific customer needs in agreed areas and tailored to work in these specific locations. Such areas are, for example, fields, orchards, vineyards, forests, or specific locations such as farms, barns, or warehouses. Furthermore, the service includes access to corrections from local RTK stations and meteorological station data. In this way, it provides comprehensive and reliable data access support for a variety of customer requirements.

Qualification of trajectory execution accuracy of livestock robots

INRAE

Location

France

Livestock farming

ARPA PC2 thoroughly evaluates how well agricultural robots can follow established paths in indoor and outdoor environments. This service helps ensure that robots can successfully navigate various settings typical of a farm, including any obstacles and transitions between spaces they might face. ARPA PC2 provides essential information about the robot's accuracy and behaviour while moving by testing in real-life conditions. This assessment enhances operational reliability and safety and supports manufacturers in optimising their robots for improved performance in diverse agricultural scenarios.

Safety and Regulatory Testing for Agricultural Robotics

Danish Technological Institute (DTI)

Location

At user's premises

Denmark

Arable farming

Food processing

Greenhouse

Horticulture

Livestock farming

Tree Crops

Viticulture

This service offers a thorough examination of agricultural robots, focusing on safety validation under diverse operational conditions. It tests stopping distances and response times for mobile robots across various speeds and payloads. Regulatory compliance is assessed to ensure adherence to industry standards. The service utilizes a controlled testing environment that simulates agricultural field conditions, equipped with measurement tools to analyze stopping distances and robot dynamics. Parameters can be adjusted to test different speeds, payloads, and environmental factors, minimizing risks and enhancing reliability in dynamic farm environments.

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.

Support for testing safety devices for safe obstacle avoidance by autonomous vehicles

Lukasiewicz Poznanski Instytut Technologiczny (L-PIT)

Location

Poland

Arable farming

Horticulture

Livestock farming

Tree Crops

Our service offers to provide specialised support for evaluating the behaviour of a self-moving robot or autonomous vehicle. As part of the service, the equipment and control systems will be verified for the maintenance of operational safety rules by analysing the correctness of stopping in front of obstacles. For this purpose, an advanced testing methodology based on the provisions of ISO 18497 will be used. During the tests, specialised equipment will be used to control the maintenance of the linearity of vehicle travel. Support will be tailored to the customer's needs in terms of providing the required control equipment and elements necessary for proper testing, such as model obstacles. These activities lead to increased automation efficiency and precision of controlled objects in agricultural applications.

Support in interconnecting systems to AgrifoodTEF’s computational test infrastructure

Politecnico di Milano (POLIMI)

Università degli Studi di Milano (UMIL)

Location

Italy

Remote

Arable farming

Food processing

Greenhouse

Horticulture

Livestock farming

Tree Crops

Viticulture

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).