Catalogue of Services

ARPA 1 - Qualification of safety systems providing obstacle detection and robot safety functions (ISO 18497:2024 reference obstacle)

INRAE

Location

France

Arable farming

Greenhouse

Horticulture

Livestock farming

Tree Crops

Viticulture

We offer a comprehensive testing service to validate the safety functions of autonomous mobile robots and robotic systems. Our tests use a high-precision laser tracker for accuracy to assess your robot's ability to detect and stop when encountering an obstacle, as defined in ISO 18497:2024 (Protocol included in part IV Annex D.2). 

These tests, conducted at our Montoldre site in France, will help ensure your robot's compliance with industry safety standards. A representative from your team must be present to assist in configuring and monitoring the system during testing.

ARPA 2 - Qualification of perception systems in harsh environmental conditions (rain, fog, night) 

INRAE

Location

France

Arable farming

Greenhouse

Horticulture

Livestock farming

Tree Crops

Viticulture

This service tests the performance of on-board detection systems in agricultural robots, focusing on their safety functions under challenging environmental conditions like fog and rain. Conducted at the PAVIN platform in Clermont-Ferrand, France, the tests evaluate sensor accuracy and response, identifying any false negatives or positives. A manufacturer's representative must be present to configure the system and ensure proper operation during testing.

ARPA 3 - Qualification of safety functions ensuring robots keep within their working area (physical or virtual barriers)

INRAE

Location

France

Arable farming

Greenhouse

Horticulture

Livestock farming

Tree Crops

Viticulture

This service evaluates the effectiveness of onboard geofencing safety systems in agricultural robots, ensuring they stay within designated work areas as required by ISO 18497:2024. The test involves the robot following various trajectories that challenge boundary limits. High-precision monitoring ensures compliance, with the robot required to enter a safe state if it breaches the boundary. A manufacturer's representative must be present to oversee the process. 

ARPA 4 - Qualification of safety systems for human detection and collision avoidance (diverse working environments)

INRAE

Location

France

Arable farming

Greenhouse

Horticulture

Livestock farming

Tree Crops

Viticulture

The ARPA 4 test thoroughly evaluates the safety systems of agricultural robots, focusing on their ability to detect human-like obstacles and automatically stop to prevent accidents. It simulates real-world scenarios by positioning mannequins in front of the robot as it moves through artificial crop rows. Using a high-precision laser tracking system, the test measures the robot’s trajectory and speed at our Montoldre site in France. This ensures the robot can safely operate in environments like vineyards or orchards, providing valuable insights into its emergency stop functions and compliance with safety standards. A manufacturer’s representative must oversee the process.

Assessment of dataset quality

Laboratoire National de Meterologie et d'Essais (LNE)

Location

Remote

Arable farming

Food processing

Greenhouse

Horticulture

Livestock farming

Tree Crops

Viticulture

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.

Assessment of Hazard and Risk for Autonomous Agricultural Robots

INRAE

Location

Remote

Arable farming

Greenhouse

Horticulture

Livestock farming

Tree Crops

Viticulture

Designing agricultural robots that operate safely and meet regulatory expectations is essential for market deployment and stakeholder trust. This service offers a structured risk assessment tailored to autonomous robotic systems in agriculture. It supports manufacturers by:  ✔ Reviewing system architectures and identifying potential points for improvement.  ✔ Reviewing estimation risk levels and appropriate safety integrity targets.  ✔ Ensuring alignment with safety standards such as ISO 18497, ISO 25119, and ISO 13849.  Through a third-party expert perspective, the service strengthens your risk management approach and enhances the credibility of your safety documentation. 

Assistance for Identifying AI Technologies for Agricultural Robotics, Drones, and Precision Machinery

Danish Technological Institute (DTI)

Location

Denmark

Remote

Arable farming

Food processing

Greenhouse

Horticulture

Our service is tailored to support AI technology providers, particularly in robotics and drone sectors, focusing on agricultural applications. We identify AI technologies that boost agricultural efficiency and productivity and offer integration support for embedding AI into agricultural robotics, drones, and precision machinery. Our guidance includes selecting effective AI tools and algorithms for specific agricultural challenges. Additionally, we offer comprehensive screening of AI technologies for agricultural relevance, which supports the company’s seamless integration of AI into existing equipment.

Certification of AI processes by LNE

Laboratoire National de Meterologie et d'Essais (LNE)

Location

At user's premises

Arable farming

Food processing

Greenhouse

Horticulture

Livestock farming

Tree Crops

Viticulture

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

Custom AI-Optimized Testing Protocol Design for Agricultural Solutions

Danish Technological Institute (DTI)

Location

Denmark

Arable farming

Greenhouse

Horticulture

Livestock farming

We specialise in designing comprehensive testing protocols for the physical testing of customer solutions in the agricultural sector. Our approach focuses on creating consistent procedures that enhance the precision of AI models developed from the collected data. These protocols are carefully crafted to meet individual customer and project needs, incorporating requirements for both laboratory and field testing scenarios. We prioritise automation in data collection to ensure consistent and repeatable testing procedures, which is crucial for high-quality data collection. This service bridges the gap between physical testing and AI model development, providing a solid foundation for advancing agricultural technologies.

Data Collection for Advanced Agricultural Environmental Monitoring and Analysis Systems

Danish Technological Institute (DTI)

Location

Denmark

Arable farming

Greenhouse

Horticulture

Livestock farming

We offer cutting-edge technical systems, both hardware and software, along with expert knowledge for collecting and analysing relevant environmental data during agricultural testing. Our service emphasises the use of standardised protocols to ensure enhanced accuracy and efficiency. We provide a comprehensive suite of monitoring and analysis tools, including methane detection systems, PTR-MS for volatile organic compound analysis, GHG measurement using CRDS technology, and automated climate data collection using IoT sensors and AI algorithms. These advanced systems enable precise environmental monitoring and data analysis for agricultural research and development.

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.