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Safety and Regulatory Testing for Agricultural Robotics

Danish Technological Institute

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.

Collection of test data

Conformity assessment

Setup for AI solution testing

Fondazione Bruno Kessler

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.

Test setup

Testing AI Solutions for Precision Agriculture

Danish Technological Institute

This service focuses on testing and experimenting with AI solutions specifically designed for agricultural applications. It aids companies in robotics and drones by facilitating the implementation and deployment of advanced technologies in farming and food processing environments. The service includes the evaluation of new sensors and algorithms that enhance localisation and positioning, crucial for precise agricultural operations. We offer testing and validation of AI-driven solutions for precision agriculture, support for implementing robotics and AI in agricultural practices, optimisation of sensor and algorithm performance for enhanced crop monitoring and yield prediction, and deployment of drones for aerial surveillance and data collection on large farms.

AI model training

Collection of test data

Data analysis

Performance evaluation

Test design

Test execution

Test setup

Testing and evaluating sensor devices

National Institute for Research in Digital Science and Technology  - INRIA

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.

Collection of test data

Data analysis

Desk assessment

People training

Performance evaluation

Provision of datasets

Test design

Test execution

Test setup

Testing and evaluating the robot’s functionalities

National Institute for Research in Digital Science and Technology  - INRIA

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

Collection of test data

Data analysis

Desk assessment

People training

Performance evaluation

Test design

Test execution

Test setup

Testing and evaluation of mobility algorithms with ground robots

National Institute for Research in Digital Science and Technology  - INRIA

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.

Collection of test data

Data analysis

Desk assessment

Performance evaluation

Test design

Test execution

Test setup

Testing and evaluation of mobility algorithms with aerial robots

National Institute for Research in Digital Science and Technology  - INRIA

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).

Collection of test data

Testing and validation of the performance of predictive models based on nondestructive spectral sensor data (NIR and hyperspectral data).

University of Cordoba

This service validates the performance of multivariate predictive models that utilise nondestructive spectral sensor data, such as NIR and hyperspectral imaging. Through statistical and chemometric analysis, the service assesses model accuracy and provides recommendations for optimization. Clients benefit from access to a comprehensive software suite dedicated to chemometrics and statistical studies, as well as options for database transfer to adapt models across devices. The service also includes access to a large agri-food sample bank for model evaluation and refinement.

AI model training

Collection of test data

Conformity assessment

Data analysis

People training

Performance evaluation

Testing of error handling, failure monitoring, and safety related to cyber threats

Lukasiewicz - Poznanski Instytut Technologiczny

As part of the service, its provider offers to grant access to the possibility to perform and carry out specialised tests for artificial intelligence systems and robots, aimed at detecting system faults, monitoring failures, and providing reliable assessments of cybersecurity risks present in the operation of IT systems, computer systems, data transmission, etc. These are now necessary measures to ensure the safe, reliable, and secure operation of artificial intelligence systems, autonomous vehicles, and robots. They require continuous testing by their user, the owner, which goes beyond basic functionality. In addition, continuous monitoring of failures through sensor data, system logs, and performance indicators is crucial to identify potential problems before they escalate. These activities are also part of a continuous cybersecurity threat assessment of owned ICT systems, databases, etc.

Cybersecurity

Performance evaluation

Testing of translation solutions for multilingual applications

Fondazione Bruno Kessler

Agriculture is a local and specific business, where localisation of solutions is required, especially when it comes to language and interfacing with users. Albeit the diffusion of multilingual generative AI technologies (or large language models, e.g., GPT), these models are not optimised for a specific industry. We can test the performance of multilingual AI solutions in agriculture, highlighting bottlenecks that limit them. This allows for optimised localisation of solutions and quicker achievement of scalability at the European level.

Performance evaluation

Test design

Test execution

Test setup

Tracking of AI development lifecycle

This service helps you keep track of everything that happens while training and testing AI systems. Think of it like a detailed diary that automatically records all the important information about your AI models. Every time you make changes to your model, train it with new data, or test its performance, our system saves this information in an organised way. You get access to a user-friendly website where you can see all your models, compare how well different versions are performing, and understand what changes led to better results. For example, if you're training an AI model to count fruits on trees, you can easily compare how accurate different versions of your model are, see which training data worked best, and track how each change improved the counting accuracy. The system stores all the technical details, test results, and performance measurements in one place, making it simple to find information when you need it and share results with your team. This takes away the headache of manually documenting everything and helps you make better decisions about which version of your AI model to use.

AI model training

User Experience (UX) Evaluation

Universitat de Lleida

This service is offered by the UsabiliLAB, which specialises in advancing the usability and accessibility of interactive products and services. The service helps companies, particularly SMEs, develop AI and robotics solutions that are not only innovative but also more efficient, effective, and user-friendlier, while considering their accessibility.

The UsabiliLAB offers assessments, including conformity regarding relevant standards, and provides actionable insights to enhance usability and accessibility across various platforms, including:

Web Pages: We evaluate and refine agricultural web platforms to make them more intuitive and accessible.
Mobile Applications: Our detailed assessments help improve the usability of mobile apps, making them more user-friendly and accessible to a wider audience.
Other Interactive Products: from software interfaces to agricultural machinery, we analyse and improve any product with a user interface, ensuring that they are easy to use and meet the needs of your target audience.

Collection of test data

Conformity assessment

Catalogue of Services

All agrifoodTEF services can be customised!