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.

Agronomic Validation of AI and Robotic Technologies

Universitat de Lleida (UdL)

Location

At user's premises

Spain

Arable farming

Horticulture

Greenhouse

We offer experimentation fields for arable crops, horticulture crops, and greenhouse facilities. These environments can be complemented with agronomic validation services, sample laboratory analysis or crop characterisation if required.

All combined serve as testbeds for data acquisition throughout the crops’ growth cycle, providing the infrastructure and expertise to support companies in the testing, validation and improvement of their technological solutions in real agricultural conditions.

This includes:

• AI-based solutions, such as training and calibration predictive models and algorithms for agronomy applications, including automatic weed detection, biomass and yield estimation, phenological characterisation, or pest and disease monitoring.
• Robotic systems and machinery that require physical testing, such as autonomous vehicles, precision spraying equipment, harvesting robots, or treatment platforms.

Assessment of AI and robotic solutions for testing

National Institute for Research in Digital Science and Technology  (INRIA)

Josephinum Research (JR)

RISE - Research Institutes of Sweden

Universitat de Lleida (UdL)

Location

France

Austria

Sweden

Spain

Remote

Arable farming

Food processing

Greenhouse

Horticulture

Livestock farming

Tree Crops

This service includes all essential actions to gather knowledge, design, and document the client’s needs regarding Testing and Experimentation Facility (TEF) services. Through close collaboration with the client, we ensure that all technical, operational, and methodological aspects are clearly outlined, forming a solid foundation for future testing phases. By conducting these preparatory activities, we deliver a comprehensive setup analysis that serves as the entry point for subsequent testing and validation services and provision of datasets. This ensures that testing can be carried out efficiently, aligning with the client’s specific requirements while leveraging TEF’s expertise and infrastructure. Ultimately, this service streamlines the testing process, reduces uncertainties, and enhances the overall efficiency of AI and robotic solution validation.

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

Danish Technological Institute (DTI)

HISPATEC

Location

Denmark

Remote

Spain

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.

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.

Collection of test data during physical testing

Politecnico di Milano (POLIMI)

Università degli Studi di Milano (UMIL)

Universitat de Lleida (UdL)

Location

Italy

Spain

Arable farming

Food processing

Greenhouse

Horticulture

Livestock farming

Tree Crops

Viticulture

Through this service, we offer dedicated technical systems (hardware and software) and expertise for the collection of relevant data during testing.

The scope of data collected can range from data generated by the AgrifoodTEF technical infrastructure (e.g., through the installation of IoT sensors) to the collection of data generated by the system under test to the generation of ground truth for evaluating the system. If necessary, service S00111 may be leveraged to take care of the interconnection between AgrifoodTEF data collection systems and the customer’s systems.

In addition to the raw collected data, we will also provide customers with complete documentation describing the logged features and conditions of the testing environment at the time of testing, as well as any parameter values, variation ranges and specifics required for reproducibility purposes.

Where necessary, data collection and documentation activities can require the involvement of field experts (e.g., engineers, agronomists).

Note: annotation and labelling of collected data, if needed, can be provided via service S00115.

Computer vision for automated food quality assessments

Universitat de Lleida (UdL)

Location

Remote

Spain

Food processing

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.

Data Analysis for Smart Irrigation Systems

HISPATEC

Location

Spain

Arable farming

Greenhouse

Horticulture

Tree Crops

Viticulture

This service allows companies to experiment with real-time data and advanced analytics to improve water management, reduce waste, and enhance crop yield. The service includes comprehensive testing and evaluation to ensure compatibility and performance, ensuring efficient irrigation practices and sustainability through insights into soil moisture, weather conditions, and plant water needs.

Data validation and processing

Politecnico di Milano (POLIMI)

Università degli Studi di Milano (UMIL)

Universitat de Lleida (UdL)

Location

Italy

Spain

Remote

Arable farming

Food processing

Greenhouse

Horticulture

Livestock farming

Tree Crops

Viticulture

Through this service, we offerers the ability to validate and augment the data collected during the different testing phases. Examples of operations that this service can provide include quality checks (e.g., presence of all datastreams, presence of drop-outs, technical quality such as focus or exposure of camera images, agronomic significance of data); preparation for performance evaluation (e.g., identification of the images containing selected visual markers or specific plants, association between images from the system under test and the ground truth); data processing (e.g., transcoding); and augmentation (e.g., generation and incorporation of metadata). Data labelling (e.g., to enable training of AI models) is not included as it is the object of a separate service (S00291).

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 evaluation metrics for physical testing

Politecnico di Milano (POLIMI)

Università degli Studi di Milano (UMIL)

Universitat de Lleida (UdL)

Location

Italy

Spain

Remote

Arable farming

Food processing

Greenhouse

Horticulture

Livestock farming

Tree Crops

Viticulture

Any physical test activity involves three main components: environment (where the tests take place), protocol (defining what tests are executed and how) and evaluation metrics (used to assess the results of the tests). This service concerns the last element; its goal is to design the best metrics to evaluate the performance of a customer solution, taking into consideration the use cases specified by the customer and the environment and protocol chosen for the tests (which, if needed, can be designed via services S00106 and S00107).

Our team will identify and define with customers the most adequate set of quantitative (i.e., based on instrumental measurements) and/or qualitative (i.e., relying on expert human judgement) metrics to assess the system functionalities of interest. This phase will involve, in particular, agronomists and experts in agricultural machinery.

Based on the defined evaluation metrics, a set of requirements for the collection of required data and ground truth annotations will also be defined accordingly. For instance, the service may lay out the specifications for dedicated data collection campaigns (possibly executed via service S00113). This phase will involve engineers and experts in AI and robotics.

On request, the output of the service will include analyses on additional environmental factors than those directly tracked through the designed metrics (e.g., seasonal effects, impact of test distribution over time on results).

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.