Catalogue of Services

This service provides an in-depth evaluation of automation and robotics technologies integrated into machinery for speciality crop operations, including tillage, cover crop management, pesticide application, and harvesting. Through field testing, the service assesses automated systems, robotic vehicles, and their interoperability with other machinery. It is designed to help clients validate the functionality, efficiency, and sustainability of advanced machinery technologies used in precision agriculture for speciality crops.

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

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

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 and quality evaluation for agricultural equipment and AI algorithms

This service provides independent evaluation of agricultural datasets to determine their suitability for use in the testing, development, or validation of AI- and robotics-based systems. The focus is on verifying the quality, structure, and statistical consistency of the data to ensure it meets the requirements for use in intelligent technologies operating in agricultural environments. Our evaluation process includes the application of descriptive statistical techniques to assess data completeness, identify anomalies, and quantify variability across key parameters such as crop yields, irrigation records, and fertilisation schedules. We assess measures of central tendency, dispersion, and distribution to evaluate the stability and reliability of the datasets. This helps identify issues like missing values, outliers, or inconsistencies that could compromise the performance or fairness of automated systems trained or tested on this data. By systematically evaluating data integrity and structure, we help researchers, developers, and integrators ensure their AI algorithms or robotic platforms are tested with datasets that reflect real-world conditions. This contributes to more effective experimentation, better system generalisation, and ultimately, more trustworthy agricultural technologies.

Evaluation and testing on the efficiency of SaaS platforms

Evaluation and analysis of the efficiency of algorithms integrated into SaaS platforms that perform specific processes, such as the calculation of parameters from processed data. The primary objective is to detect weak points in the performance of the platform and evaluate the execution times.

People training

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.

Laboratoire National de Meterologie et d'Essais (LNE)

Evaluation of AI solutions performance based on testing datasets

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 gives you a competitive edge, as their certification enhances credibility and opens doors to new market opportunities both locally and globally. LNE's AI performance evaluation service uses comprehensive testing datasets to assess the accuracy, robustness, and efficiency of your AI systems by comparing the outputs of the system with a dataset of reference values. By testing real-world scenarios, LNE ensures that your AI models meet industry standards and regulatory requirements, helping you improve performance, reliability, and market readiness. LNE utilises a diverse range of carefully curated datasets that simulate various operating conditions and environments in which the AI may be deployed. These datasets allow for in-depth testing of the system’s ability to process information, make decisions, and produce accurate outputs. The service covers a broad spectrum of AI applications, from machine learning models and deep learning algorithms to computer vision systems, natural language processing (NLP), and autonomous robotics. The evaluation process examines key performance metrics such as accuracy, precision, recall, response time and scalability. It also identifies any potential biases in the system, ensuring that the AI behaves fairly and ethically across different user groups or environmental variables.

Evaluation of crop classification at parcel scale functionality in an agricultural crop management platform

The functionality of crop classification at the parcel level will be assessed using satellite imagery through a platform specialised in agricultural crop management. To evaluate quality, CAP data from prior agricultural seasons will be utilised. This validation process will be applied in Andalusia, Spain, with a focus on the region's agroclimatic conditions and crop phenology.

Evaluation of customer UAV-collected test data from physical testing

This service applies pre-defined performance metrics to UAV-collected test data from customers and corresponding ground truth data to assess the effectiveness and reliability of tested systems. The service includes a detailed evaluation and reporting process, offering clients valuable insights into system performance and supporting their technological validation needs. This evaluation is crucial for clients looking to understand the accuracy and robustness of their UAV-based data collection tools in agriculture.

Evaluation of monitoring technologies for parameters in harvesters and equipment used in the pre- and post-harvest phases of speciality crops

This service provides physical testing and evaluation of parameter monitoring technologies integrated into harvesters and post-harvest equipment for speciality crops, particularly in olive farming. Field assessments cover a range of technologies, including yield monitors, harvest mapping, fleet management systems, remote diagnostics, and fruit evaluation tools. By focusing on the pre- and post-harvest phases, this service ensures that sensor performance and measurement accuracy meet industry standards, supporting sustainable and precise agricultural practices.

Evaluation of onboard technologies in tractors and machinery

Location

Spain

Tree Crops

This service provides comprehensive field testing for onboard technologies in tractors and machinery. The evaluation includes the functionality of sensors and measurement systems integrated into the machinery, covering production mapping, fleet management, remote diagnostics, traceability, and the use of machinery simulators. The service supports clients in validating the effectiveness of their equipment for sustainable operations and improving technology-driven management.