Catalogue of Services

Assessment of AI and robotic solutions for testing

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

Josephinum Research (JR)

RISE - Research Institutes of Sweden

Location

Austria

France

Remote

Sweden

Arable farming

Food processing

Greenhouse

Horticulture

Livestock farming

Tree Crops

This service includes all essential actions to gather knowledge, design, and document the client’s needs regarding Testing and Experimentation Facility (TEF) services. Through close collaboration with the client, we ensure that all technical, operational, and methodological aspects are clearly outlined, forming a solid foundation for future testing phases. By conducting these preparatory activities, we deliver a comprehensive setup analysis that serves as the entry point for subsequent testing and validation services and provision of datasets. This ensures that testing can be carried out efficiently, aligning with the client’s specific requirements while leveraging TEF’s expertise and infrastructure. Ultimately, this service streamlines the testing process, reduces uncertainties, and enhances the overall efficiency of AI and robotic solution validation.

Assisting in Testing and Data Collection of AI-Driven Robotic Solutions

Lukasiewicz Poznanski Instytut Technologiczny (L-PIT)

Location

At user's premises

Poland

Arable farming

Food processing

Greenhouse

Horticulture

Livestock farming

Tree Crops

Viticulture

Our service provides end-to-end support for testing and data collection processes involving robotic or AI-driven systems. We assist in planning and conducting tests to ensure compliance with defined methodologies and objectives. Services include verifying system configurations, monitoring operations, managing technical infrastructure, and ensuring seamless data exchange. If necessary, we can provide access to modern agricultural machinery, such as advanced tractors or field robots equipped with Agriculture 4.0 systems, various sensors, and data recording systems. Additionally, we supply skilled operators to facilitate testing. Our service can also leverage our specialised physical infrastructure, including carrier frames, measurement wheels, and components for rapid prototyping and servicing, ensuring smooth testing operations. Collected data can be processed, documented, and provided in formats suitable for further analysis, enabling you to optimise your solutions effectively.

Benchmarking & Testing Suite for Edge Hardware Systems

Lukasiewicz Poznanski Instytut Technologiczny (L-PIT)

Poznan Supercomputing and Networking Center (PSNC)

Location

Poland

Arable farming

Food processing

Greenhouse

Horticulture

Livestock farming

Tree Crops

Viticulture

The Benchmarking & Testing Suite for Edge Hardware Systems delivers a set of tests designed to evaluate the performance, reliability, and functionality of edge hardware and its components under various operational conditions. 

The tests can be compared to specific industry standards or the performance of other solutions available on the market. Examples of tests offered as part of the service: 

- Environmental tests: Assessing devices' resistance to extreme conditions, such as temperature, humidity, and vibrations. 

- Signal tests: Evaluating devices using GNSS signal generators, testing their resilience to interference or false signals. 

- Network tests: Evaluate device performance within a prototype 5G network infrastructure.

 - Functional tests: Assessing the capability of devices, such as remote PTZ (pan, tilt, zoom) cameras, to perform operational tasks in field conditions. 

- Integration tests: Examining the cooperation of edge devices with sensors, AI systems, and their responses to data input failures. - Accuracy tests: Measuring the precision of sensors and control systems. Test results can be compared to specified standards or the performance of competitive solutions, enabling customers to better understand their devices' capabilities.

Conformity assessment and compliance tests

Lukasiewicz Poznanski Instytut Technologiczny (L-PIT)

Location

At user's premises

Poland

Arable farming

Food processing

Greenhouse

Horticulture

Livestock farming

Tree Crops

Viticulture

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 resulting from for instance New Legislative Framework directives and other EU law are fulfilled to better protect both users and agrifoodTEF professionals from unsafe prototypes of products being subjected to other afT services in the next stages. One of the aims is to help manufacturers is to verify the prototype before constructing final agrifood products to be placed on EU single market. The technical scope of test may result from requirements of EMC, LVD, RED, MD, MR on other NLF directives and regulations but is limited to prototypes.

Curation and provision of annotated datasets

Lukasiewicz Poznanski Instytut Technologiczny (L-PIT)

Poznan Supercomputing and Networking Center (PSNC)

Location

Poland

Remote

Arable farming

Food processing

Greenhouse

Horticulture

Livestock farming

Tree Crops

Viticulture

The service provides access to annotated data collected from devices tested as part of other project services. It includes both data annotation services (manual and automatic) and the provision of annotated databases or data packages. In addition, we offer disc space for temporary archiving of the collected data along with its annotation in cases where this is not possible at the client's site. As a result of the service, procedures for both manual and automatic annotation will be identified that are appropriate to the type of measurement data and the way it is collected and assembled. The methods of labelling and assigning specific data classes collected by the agriFoodTEF technical infrastructure will be proposed. Possibilities for documenting and storing data in datasets will be indicated. In addition, procedures for extracting selected features from stored research data can be offered.

Data harmonisation

Poznan Supercomputing and Networking Center (PSNC)

Location

At user's premises

Poland

Remote

Arable farming

Food processing

Greenhouse

Horticulture

Livestock farming

Support for data harmonisation and integration, i.e., processing and transforming data from different sources into a standard common format. This process requires the specification and implementation of mappings between a data source model and a target (standard-based) model, the integration with other standard-based data sources (establishing links between data elements), and the provision of interfaces to access the harmonised data. The harmonisation can be performed as a one-time activity, e.g., with (semi-) static data sources, but more importantly in a continuous manner, whenever new data is becoming available. For the latter, a pipeline is defined with the specified mapping once, and then it can be re-executed (via CLI or API) as frequently as needed just by changing the input data.

Data validation and processing services

Lukasiewicz Poznanski Instytut Technologiczny (L-PIT)

Location

Poland

Remote

Arable farming

Food processing

Greenhouse

Horticulture

Livestock farming

Tree Crops

Viticulture

The service will provide validation and processing of data sets, in particular video data, coming from AI-based systems testing and/or digital measurement and data collection in cloud technologies. The scope of the proposed data validation will include, among other things, the service provider's activities of checking the presence of all data streams in the analysed image or video files, detecting any breaks in transmission, and checking the focus or exposure of the camera images. The next stage of the service will be the preparation of the material for evaluating the performance of AI-based technical systems under test, e.g., by formatting, identifying images containing selected visual markers, or linking images from a given system under test to data of other modalities.

Evaluation of AI performance based on mixed testing environments

Laboratoire National de Meterologie et d'Essais (LNE)

Location

France

Remote

Arable farming

Food processing

Greenhouse

Horticulture

Livestock farming

Tree Crops

Viticulture

By having your AI system tested by the LNE, you ensure it meets the highest standards of safety and performance, boosting your product’s reliability and trustworthiness in the market. Partnering with the LNE for rigorous testing of your AI technologies and a detailed evaluation report used for demonstrating performance enhances credibility and opens doors to new market opportunities both locally and globally. This service proposes to assess a range of agricultural devices (with respect to the physical constraints of the testing bench) integrating AI, particularly those utilising vision processing, within our advanced hybrid testing environment. Our novel hybrid testing facility consists of placing devices in the heart of a simulation projected onto a 300° screen while a motion capture system and instrumented conveyor belt measure its movements, if any. These data are continuously sent to the simulator in real time so that the device's digital twin follows the same movements and the projected environment is updated accordingly. The simulator also incorporates a physics engine and advanced sensor models, enabling a virtual sensor output to be substituted for the sensors in real time in the cases where devices require specific data and/or if the assessment is orientated toward a special kind of sensor degradation. Typical agricultural products evaluated include mobile robots for autonomous weeding or harvesting that use visual navigation (based on 2D cameras) and intelligent cameras (with AI functionalities) for crop health monitoring or livestock tracking. Other sensors commonly used in agriculture, such as 3D cameras for yield estimation, GPS for autonomous vehicle control, Lidar for terrain mapping, and sonar for obstacle detection, are also supported through data injection from the simulator.

Evaluation of AI solutions for crops, livestock, and food products analysis

Lukasiewicz Poznanski Instytut Technologiczny (L-PIT)

Location

Poland

Remote

Arable farming

Food processing

Greenhouse

Horticulture

Livestock farming

Tree Crops

Viticulture

The service provides the testing, validation, and training of machine learning algorithms specialised in the detection of weeds, crops, livestock, and food products. The model data for the training implementation can come from different sources. They will come from services run by the Agrifood TEF service network. They may come from the service provider's implementation of other similar services, or they may be provided by the client of the service as collections resulting from their research. In this case, the datasets provided will be validated to assess their quality and suitability for machine training of artificial intelligence algorithms. The envisaged scope of the service, the algorithm training, relates to a wide range of fields and includes farming and plant breeding, animal husbandry, and food processing.

Evaluation of the energy efficiency of the embedded hardware associated with the AI functionality

Laboratoire National de Meterologie et d'Essais (LNE)

Location

France

Arable farming

Food processing

Greenhouse

Horticulture

Livestock farming

Tree Crops

Viticulture

By having your AI or robotic system tested by LNE, you ensure it meets the highest standards of safety and performance, boosting your product’s reliability and trustworthiness in the market. Partnering with LNE for rigorous testing of your robotic and AI technologies gives you a competitive edge and trusted third-party assessment that enhances your product credibility and opens doors to new market opportunities both locally and globally. This service provides testing of devices with AI-integrated control systems, focusing on energy efficiency and consumption during the product life cycle. By simulating a wide range of scenarios, we provide detailed insights into the energy performance of these AI systems with regard to their expected operational environment. These tests can be conducted under different conditions, either in a completely simulated way, through a hybrid test bench where the actual physical device is assessed within a simulated environment, or using physical infrastructures based on the device's operational environment to test it in a 'real' setting. Tests are conducted under controlled laboratory conditions, ensuring optimal reproducibility and repeatability, providing reliable insights into the system's efficiency and safety-critical functionalities. Typical assessments may cover the energy efficiency of SLAM, various types of end effectors, and other relevant components such as sensors, motors or AI software. This rigorous testing process helps identify potential areas for improvement, enabling companies to enhance the efficiency and sustainability of their products, ultimately leading to cost savings and reduced environmental impact.

Experimentation with synthetic data generation and data augmentation techniques

Lukasiewicz Poznanski Instytut Technologiczny (L-PIT)

Poznan Supercomputing and Networking Center (PSNC)

Location

Poland

Remote

Arable farming

Food processing

Greenhouse

Horticulture

Livestock farming

Tree Crops

Viticulture

This service involves expanding training datasets using synthetic data and data augmentation techniques. This will use algorithms capable of generating synthetic data simulating certain aspects of existing datasets. Such an approach will lead to the enrichment of the existing training material. The service includes the creation of synthetic replicas based on real data, the combining of real and synthetic data, and also, for example, the creation of digital terrain maps based on ground data, which can be further enriched with synthetically generated details. As a result, we offer a comprehensive solution for more reliable development of datasets.