Catalogue of Services

As part of this service, we test and measure, among other things, mechanical, physical, acoustic, radio, electromagnetic compatibility and electrical parameters for the purpose of assessing the overall user safety of agricultural, horticultural, forestry and food machinery, equipment and components. Verification is based on the requirements of standards and directives declared by the manufacturer. We carry out an initial assessment based on the design documentation provided or/and on measurements taken on a prototype based on harmonised and non-harmonised standards with relevant EU and sectoral legislation. This is to ensure that for instance essential requirements coming from for instance New Legislative Framework directives and other EU law are fulfilled to better protect both consumers and professionals from unsafe products to be placed on the European internal market. One of the aims is to help manufacturers in legal placing agrifood products on EU single market and in CE-marking process. The results obtained can be used in the further process of product labelling, declaration of conformity to affix the CE mark to the device within the scope of EMC, LVD, RED, MD, MR on other NLF directives and regulations.

Certification

Conformity assessment

Performance and sustainability testing of robotic or AI-driven solutions

This service provides a comprehensive assessment of the performance of systems, focusing on sustainability indicators such as energy consumption and input usage per hectare. It evaluates the impact of artificial intelligence (AI) or robotic solutions on the performance and sustainability of the associated equipment. The service includes tests such as functional testing, accelerated durability testing, and assessments of the system’s resilience to environmental factors and electromagnetic interference (EMI). By identifying potential design flaws or manufacturing defects, the service supports the development and optimisation of tested solutions, contributing to sustainable technological advancements.

Performance Testing of Robotic Systems and AI Solutions

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 of Endurance & Reliability

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.

People training

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.

Data augmentation

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.

Data augmentation

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.

Data augmentation

Provision of real-time data streams

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.

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

Testing and evaluating sensor devices

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.

People training

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

Testing and evaluating the robot’s functionalities

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

People training

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

Testing and evaluation of mobility algorithms with ground robots

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.

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

Testing and evaluation of mobility algorithms with aerial robots

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).