Catalogue of Services

Validation of an existing automated food quality assessment solution based on Artificial Intelligence, in particular Computer Vision techniques. The service will help the solution provider check its performance using a controlled testing infrastructure, including our Food Processing Pilot Plant. The service might also include the generation of reference datasets of food samples and expected quality measures, which can be used both for validation and training of the Artificial Intelligence solution based on Computer Vision.

AI model training

Computer Vision for automated food quality assessment

Validation of an existing automated food quality assessment solution based on artificial intelligence, in particular computer vision techniques. The service will help the solution provider check its performance using a controlled testing infrastructure, including our Food Processing Pilot Plant. The service might also include the generation of reference datasets of food samples and expected quality measures, which can be used both for validation and training of the artificial intelligence solution based on computer vision.

AI model training

Institut de l'elevage (IDELE)

Data and media labelling for AI training

Location

Remote

Tree Crops

This service specializes in accurately annotating and labeling datasets or multimedia content, including photos, videos, and audio, specifically for training Artificial Intelligence algorithms in the ruminant livestock sector. By meticulously tagging key features such as animal behavior, physical conditions, environmental factors, and health indicators, we provide high-quality datasets that enhance the precision of AI models used in livestock monitoring, health assessment, and farm management solutions.

Our service ensures that AI solutions for ruminant farming are trained with contextually rich, well-labeled data, enabling the development of advanced tools for animal welfare, productivity optimization, and predictive analytics in the livestock industry.

Data augmentation

Data and media labelling for AI training

Institut de l'elevage (IDELE)

This service specialises in accurately annotating and labelling datasets or multimedia content, including photos, videos, and audio, specifically for training artificial intelligence algorithms in the ruminant livestock sector. By meticulously tagging key features such as animal behaviour, physical conditions, environmental factors, and health indicators, we provide high-quality datasets that enhance the precision of AI models used in livestock monitoring, health assessment, and farm management solutions. Our service ensures that AI solutions for ruminant farming are trained with contextually rich, well-labelled data, enabling the development of advanced tools for animal welfare, productivity optimisation, and predictive analytics in the livestock industry.

Data augmentation

Evaluation of AI-based optimal pesticide dosage in tree crops

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 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 ethical aspects in agrifood technology design and development

We offer a specialised service to companies, particularly small and medium-sized enterprises (SMEs), that develop AI- and robotics-based products and services for the agri-food sector. Our service supports these companies in integrating ethical considerations throughout the design and development process of their AI- or robotics-based products and services. By utilising Value Sensitive Design (VSD) methods, we ensure that human values such as sustainability, fairness, transparency, and privacy are incorporated into technological solutions from the outset. Through stakeholder analysis, participatory design, value scenarios, and ethical reflection, we guide companies in creating responsible, innovative technologies that align with societal needs and ethical standards.

Desk assessment

ELSA assessment

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.

Provision of datasets