Catalogue of Services

Institut Francais de la Vigne et du Vin (IFV)

Data labelling for AI detection purposes in the vineyard

Location

France

Viticulture

You can provide your own dataset, without labels, or ask us to build the dataset and label it so you can train an AI model. Labelling can be made with your own tools or not, online or offline, it can be segregation or segmentation.

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.

Raumberg Gumpenstein Research & Development (RGRD)

Datasets

We are committed to promoting sustainable livestock production through the use of data. Benefit from great flexibility and customised solutions to accelerate your product development: Video, images, sensor data and more from all areas of animal production, sourced from our research barns or an extensive network of pilot farms. Choose from raw, unprocessed data or processed datasets that are ready to use. Please contact us with your specific requirements.

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.

People training

Instituut voor Landbouw-, Visserij- en Voedingsonderzoek (ILVO)

Evaluating autonomation readiness of agricultural robots

Developing autonomous agricultural robots requires more than just solid mechanics—it needs reliable software to manage navigation, task execution, and communication between components. With this service, we offer a ready-to-use open-source robotic software framework to evaluate how your platform performs autonomous operations. By integrating your hardware with our system, you can verify the performance of your steering, sensors, and actuation systems under realistic agricultural scenarios. The framework supports a wide variety of robot types and drive configurations (e.g., Ackerman, skid-steer) and provides built-in modules for GNSS-guided navigation, task scheduling, and sensor interfacing. Additionally, it also allows the development of custom add-ons to test experimental features, innovative ideas, or validate new components.

Desk assessment

People training

Wageningen University WUR

Evaluation for Ethical, Legal, and Social Aspects (ELSA Scan) for responsible AI development

What are the ethical, legal, and social aspects (ELSA) of AI-driven technology in agri-food? Mitigation of potential risks and finding opportunities are easier by identifying ELSA aspects in early development stages, when the technology is still in the making. Examples of ELSA aspects are autonomy, transparency, and bias, but also data privacy and other legal aspects. How does your AI technology deal with ELSA aspects? A combined survey and interview help AI developers to identify opportunities and issues with ELSA in the agri-food context from the perspective of end users and more broadly, for society. For example, to better align with the sustainability objectives of the AI-driven technology. The outcomes of the ELSA scan are a list of identified key ELSA aspects and high-level recommendations to further improve the AI technology.

ELSA assessment

Evaluation of AI-based optimal pesticide dosage in tree crops

Universitat de Lleida (UdL)

Evaluation of the performance of systems to optimise the dosage of pesticides based on artificial intelligence for tree crops. The service includes comparing the optimised treatment performance versus the standard procedure, measuring the pesticide and economic savings, assessing drift reduction to the environment, and improvements in environmental and biodiversity impact or the CO2 footprint of the treatment.

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.

Evaluation of Automatic Machines and Agricultural Robots

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.

Universitat de Lleida (UdL)

Evaluation of Precision Herbicide Application Systems

This service tests the performance, efficacy and efficiency of herbicide application systems that use AI and robotics to optimise spray treatments, including spot spraying booms and variable-rate applicators. It evaluates the system’s ability to apply herbicides accurately, reducing usage while maintaining effective weed control. Through controlled field experiments and real-farm testing, we assess herbicide savings, weed control effectiveness, crop yield impact and environmental benefits compared to conventional broadcast spraying methods. The service also validates the system's adaptability to different environmental conditions, such as varying soil types, weather, and crop growth stages, and it is applicable to herbaceous and arable crops, offering valuable insights for improving targeted weed control and reducing unnecessary herbicide use.