Catalogue of Services

Raumberg Gumpenstein Research & Development (RGRD)

Our service is designed to assist clients in identifying and evaluating potential agricultural applications for AI or robotics technologies originally developed for other sectors. Clients can provide detailed information about their technology, including its current applications and technical specifications. Our expert team will conduct extensive research to identify possible use cases related to the agricultural and food production sector, analysing factors such as feasibility, potential benefits, and relevance to current agricultural challenges. The tools used for this work will depend on the use case and may include, for example, online research as well as expert interviews. The customer will receive the results as a comprehensive report outlining the identified potential applications, adoptions needed and an assessment of their relevance and impact within the agricultural sector. This service enables clients to explore new markets and adapt their technologies to meet the evolving needs of modern agriculture.

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

Market research

Assessment of AI and robotic solutions for testing

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.

Laboratoire National de Meterologie et d'Essais (LNE)

Assessment of dataset quality

With the development of AI models in most sectors of society, the importance of the data used to train and test these models has been an important concern for the AI supplier. Understanding the quality of the datasets used, especially in sectors such as the agricultural and agrifood domains where data can be rare, is thus a prerequisite to ensure the correct development of AI models and build trust regarding these approaches for society. This service aims to answer this challenge by assessing the quality of datasets provided by the client. The features of the datasets that will be analyzed are based on agreed definition by the AI community: completeness, balance, diversity, accuracy... The dataset's quality assessment will begin by establishing the list of the system's influencing factors necessary for the constitution of a database. These factors will be identified both by using LNE expertise and outside sources, such as agrifood-relevant experts. Using these factors, LNE will analyse the distribution (or weight) of the various influencing factors in the databases and compare it to that under normal conditions of use in order to assess the representativity and balance of the dataset.

Conformity assessment

Data analysis

Calibration and Optimisation of Technological Quality Measurement Methods for Cereal Grain

Our service provides access to grain samples and comprehensive technological quality analysis conducted at Arvalis facilities, enabling the development of AI-powered grain analysis solutions. Clients can work with well-documented samples from one or multiple species, enriched with detailed metadata such as variety, harvest year, and collection location crucial for training and validating AI models. Beyond sample selection and preparation, our experts assist in choosing, testing, and validating analytical methods, covering rheological properties (Alvéolab), breadmaking tests, and protein content measurement (Infratec, Dumas). These high-quality datasets, combined with access to Arvalis facilities and controlled testing environments, provide an ideal foundation for developing machine learning algorithms that enhance grain quality prediction, automate classification, and optimise processing parameters. With our service, clients can accelerate the development, validation, and deployment of AI-driven grain analysis tools, ensuring they meet industry standards and deliver precise, reproducible results. Our expertise in analytical methods allows customers to refine their models, improve prediction accuracy, and scale AI solutions for real-world agricultural applications.

Collection of test data

Performance evaluation

Provision of datasets

Test execution

Laboratoire National de Meterologie et d'Essais (LNE)

Certification of AI processes by LNE

Today’s AI suppliers and AI users need to stand out from the competition, guarantee the performance of their algorithms or systems, and prepare to comply with the future European "AI Act" regulation, particularly with the obligation of transparency. With its dual expertise in certification and evaluation of AI algorithms, LNE has created a voluntary certification that meets this need. The aim of this certification standard is to define common requirements for the design, development, evaluation, and maintenance processes of all types of AI functionalities based on machine learning. It therefore covers all business sectors in which AI is used in order to guarantee the application of best practices that promote trustworthiness in AI. Website: https://www.lne.fr/en/service/certification/certification-processes-ai Webinar: https://www.lne.fr/fr/webinars/matinee-information-ia-act-certification-ia-l Technical documentation (FR): https://www.lne.fr/sites/default/files/bloc-telecharger/FTC-ISO-42001-LNE.pdf

Certification

Laboratoire National de Meterologie et d'Essais (LNE)

LCA assessment

Certification of Artificial Intelligence Management System (AIMS) of ISO/IEC 42001

The ISO/IEC 42001 is a global standard that provides a robust framework and structure within which AI systems can be developed, deployed and used responsibly. It sets out requirements and recommendations for establishing, implementing, maintaining and continuously improving an AI management system within the context of an organisation. Key controls included in the standard are risk management, AI impact assessment, system lifecycle management, performance optimisation, and supplier management. Its aim is to help organisations: Develop or use AI responsibly, Meet applicable regulatory requirements, and * Meet stakeholders' obligations and expectations. In this way, it provides concrete support to companies in optimising the use of AI by guaranteeing a level of control and confidence in the systems developed. Customers concerned: consulting firms; solution or application developers; integrators; companies integrating AI solutions purchased on the market or developed in-house into your offerings; competent authorities (decision-makers, regulators). Webinar: https://www.lne.fr/fr/webinars/iso-42001-certification-ia-lne-s Technical documentation (FR): https://www.lne.fr/sites/default/files/bloc-telecharger/FTC-ISO-42001-LNE.pdf

Certification

Conformity assessment

Desk assessment

LCA assessment

Data analysis to optimise AI model training

Our service enhances your dataset's quality using descriptive statistics to gain insights and ensure robustness. We use measures like mean, median, standard deviation, etc., to identify central tendencies and variability. We perform plausibility filtering to check for logical consistency and outlier filtering to remove data points that could skew predictions. This careful data preparation refines your dataset, boosting the accuracy and reliability of your model training. Tailored to your needs, these methods optimise your dataset for better model performance.

Data analysis

Data augmentation to optimise AI model training and prevent the model from overfitting

In preparing image or sensor data, pre-processing is crucial. We apply techniques like normalisation for data uniformity and correlation analysis to uncover patterns, enhancing interpretability. Dimension reduction optimises computational efficiency, while adding randomised noise and artificial expansion improves model generalisation. We implement anonymisation to protect data privacy and ensure precise data labelling for supervised learning. These methods, tailored to your needs, enhance your data's quality and usability.

Data augmentation

Dataset Generation Based on Agronomic Simulation Models

Our Agronomic Model-Based Dataset Generation service provides agricultural data derived from our internally developed agronomic models, designed and validated by our experts using experimental data. By simulating real-world agricultural conditions, we generate key insights such as growth stage dates for cereals, biomass production, nitrogen requirements, and carbon sequestration, offering highly reliable datasets to support crop modelling, soil analysis, climate impact assessment, and precision agriculture.

Provision of datasets

Design of Experimental Protocols for Agricultural Applications Using AI Technology or Robotics

Our service offers expertise in the design and implementation of experimental protocols for agricultural applications, ensuring scientific rigor, reliability, and reproducibility. Whether evaluating new agricultural products, testing measurement instruments, or optimising farming practices, we help clients develop tailored protocols that align with industry standards and research best practices. Our experts provide methodological guidance, data collection strategies, and statistical analysis recommendations, enabling clients to generate accurate and meaningful results. By leveraging our expertise, agribusinesses and technology developers can enhance the credibility of their evaluations, accelerate innovation, and optimise decision-making in agricultural research and development.

Collection of test data

People training

Performance evaluation

Test execution

Laboratoire National de Meterologie et d'Essais (LNE)

Evaluation of AI performance based on mixed testing environments

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.

Performance evaluation

Test execution