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 automation and robotics technologies for machinery in speciality crops

University of Cordoba (UCO)

Location

Spain

Tree Crops

Viticulture

This service provides an in-depth evaluation of automation and robotics technologies integrated into machinery for speciality crop operations, including tillage, cover crop management, pesticide application, and harvesting. Through field testing, the service assesses automated systems, robotic vehicles, and their interoperability with other machinery. It is designed to help clients validate the functionality, efficiency, and sustainability of advanced machinery technologies used in precision agriculture for speciality crops.

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.

Data analysis and quality evaluation for agricultural equipment and AI algorithms

University of Cordoba (UCO)

Location

Remote

Spain

Arable farming

Horticulture

Tree Crops

Viticulture

This service provides independent evaluation of agricultural datasets to determine their suitability for use in the testing, development, or validation of AI- and robotics-based systems. The focus is on verifying the quality, structure, and statistical consistency of the data to ensure it meets the requirements for use in intelligent technologies operating in agricultural environments. Our evaluation process includes the application of descriptive statistical techniques to assess data completeness, identify anomalies, and quantify variability across key parameters such as crop yields, irrigation records, and fertilisation schedules. We assess measures of central tendency, dispersion, and distribution to evaluate the stability and reliability of the datasets. This helps identify issues like missing values, outliers, or inconsistencies that could compromise the performance or fairness of automated systems trained or tested on this data. By systematically evaluating data integrity and structure, we help researchers, developers, and integrators ensure their AI algorithms or robotic platforms are tested with datasets that reflect real-world conditions. This contributes to more effective experimentation, better system generalisation, and ultimately, more trustworthy agricultural technologies.

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.

Design of test environments for physical testing

Politecnico di Milano (POLIMI)

Università degli Studi di Milano (UMIL)

University of Cordoba (UCO)

Location

Italy

Remote

Spain

Arable farming

Greenhouse

Horticulture

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 first element, i.e., the design of a physical testing environment for use cases such as (for instance) weeding, plant phenotyping, and precision spraying solutions. The protocol and the evaluation metrics can -if required- be designed via services S00107 and S00108.

Depending on your requirements and reference system/solution to be tested, our team will design an ad hoc setup equipped with all the required features for testing. Environmental features include, for example:

• the crop and weed species to be prepared and their growth stage,
• the plant layout and intra-row configuration,
• seasonal weather and climate-related conditions (e.g., lighting conditions, wind, rain), 
• the type of soil, moisture level, and terrain conditions (e.g., uneven terrain, presence of any slopes, and so forth),
• the technical infrastructure supporting the tests (e.g., electrical layout, network infrastructure, environmental sensors, data acquisition systems…).

In order to consider all aspects of the environment, this service involves a team comprising both engineers and agronomists.

Evaluation and testing on the efficiency of SaaS platforms

University of Cordoba (UCO)

Location

Remote

Spain

Arable farming

Horticulture

Tree Crops

Evaluation and analysis of the efficiency of algorithms integrated into SaaS platforms that perform specific processes, such as the calculation of parameters from processed data. The primary objective is to detect weak points in the performance of the platform and evaluate the execution times.

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 crop classification at parcel scale functionality in an agricultural crop management platform

University of Cordoba (UCO)

Location

Spain

Arable farming

Horticulture

Tree Crops

Viticulture

The functionality of crop classification at the parcel level will be assessed using satellite imagery through a platform specialised in agricultural crop management. To evaluate quality, CAP data from prior agricultural seasons will be utilised. This validation process will be applied in Andalusia, Spain, with a focus on the region's agroclimatic conditions and crop phenology.

Evaluation of customer UAV-collected test data from physical testing

University of Cordoba (UCO)

Location

Spain

Arable farming

Tree Crops

This service applies pre-defined performance metrics to UAV-collected test data from customers and corresponding ground truth data to assess the effectiveness and reliability of tested systems. The service includes a detailed evaluation and reporting process, offering clients valuable insights into system performance and supporting their technological validation needs. This evaluation is crucial for clients looking to understand the accuracy and robustness of their UAV-based data collection tools in agriculture.

Evaluation of monitoring technologies for parameters in harvesters and equipment used in the pre- and post-harvest phases of speciality crops

University of Cordoba (UCO)

Location

Spain

Tree Crops

Viticulture

This service provides physical testing and evaluation of parameter monitoring technologies integrated into harvesters and post-harvest equipment for speciality crops, particularly in olive farming. Field assessments cover a range of technologies, including yield monitors, harvest mapping, fleet management systems, remote diagnostics, and fruit evaluation tools. By focusing on the pre- and post-harvest phases, this service ensures that sensor performance and measurement accuracy meet industry standards, supporting sustainable and precise agricultural practices.