Catalogue of Services

Agronomic dataset generation through optical sensors

ARVALIS

Location

France

Arable farming

We offer a specialised service focused on the acquisition and interpretation of image datasets captured through optical sensors. These datasets provide valuable agronomic indicators such as Leaf Area Index (LAI), biomass, plant count, and more. Key insights that reflect crop health and growth patterns could be generated by analysing these data. These image-based datasets are crucial for training algorithms that predict various agronomic parameters, such as plant growth, height, yield potential, and other important metrics. The measurements of these indicators are carried out on several sites presenting a variety of agro-climatic conditions and allowing the collection of a representative and reliable dataset.

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.

Calibration and Optimisation of Technological Quality Measurement Methods for Cereal Grain

ARVALIS

Location

France

Arable farming

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.

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

Dataset Generation Based on Agronomic Simulation Models

ARVALIS

Location

France

Remote

Arable farming

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.

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

ARVALIS

Location

France

Arable farming

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.

Evaluation of AI solutions performance based on testing datasets

Laboratoire National de Meterologie et d'Essais (LNE)

Location

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 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 Automatic Machines and Agricultural Robots

ARVALIS

Location

France

Arable farming

We provide in-depth physical testing services for autonomous and semi-autonomous devices designed for agricultural applications such as irrigation, pest management, and weed control, with a focus on arable crops, specifically cereals, forage, flax, and potatoes. Our service evaluates multiple aspects of hardware performance under real-world farming conditions, including accuracy in task execution, reliability and consistency of results, and potential economic impact through cost savings and efficiency gains. Additionally, we assess the machine’s behaviour in real-world settings as well as its adaptability to various pedoclimatic conditions and finally its agronomic impact to ensure alignment with sustainable and productive farming practices.

Evaluation of results of physical testing

Politecnico di Milano (POLIMI)

Università degli Studi di Milano

Location

Italy

Remote

Arable farming

Greenhouse

Horticulture

Tree Crops

Viticulture

This service performs the crucial step that follows the execution of physical experimentation, i.e., evaluation of the performance of the system under test. This activity requires the application of suitable performance metrics to specific test data collected during the tests and then interpreting the results in view of the features of the system and the experimental setting. Depending on the specific use case, performance metrics can involve pure computer processing, human expertise by agronomists, or a combination of both. Application of performance metrics may include the development of custom software to extract the necessary information from experimental data and/or to apply suitable processing to the information. If the performance metrics require the data to be subjected to some form of pre-processing, Service S00115 may be leveraged to prepare the data. Besides the object of this service (i.e., evaluation of results), AgrifoodTEF can, on request, support the customer along any other aspect and phase of the physical experimentation pipeline. For instance, for the design of the environment and protocol for the test, the customer can leverage services S00106 and S00107. Preparation of the test environment can be done either by the customer or by AgrifoodTEF via service S00110; in both cases, assistance in interconnecting the system under test with AgrifoodTEF’s test infrastructure is available via service S00111. Finally, data collection during the test can, if needed, be provided by AgrifoodTEF via service S00113.

Evaluation of sensor performance for agricultural applications

ARVALIS

Location

France

Arable farming

We provide a service focused on evaluating the performance of a wide range of sensors used in agricultural applications. This includes chemical sensors (for monitoring nutrients and soil pH), optical sensors (for capturing crop health and canopy structure), thermal sensors (for detecting water stress and soil temperature), acoustic sensors (for assessing soil texture or monitoring pests), and electromagnetic sensors (for measuring soil moisture and conductivity). Our evaluation process is conducted under real-world conditions, ensuring that sensors are tested in diverse farming environments and agro-climatic scenarios. The service assesses critical parameters such as accuracy, reliability, durability, and adaptability to different crops, soil types, and operational conditions. These sensors will be integrated into solutions based on AI and robotics to enhance efficiency and decision-making.