Catalogue of Services

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.

Dataspace use case design analysis

GRADIANT

Location

Remote

Spain

Arable farming

Food processing

Greenhouse

Horticulture

Livestock farming

Tree Crops

Viticulture

Creating a dataspace involves significant costs and time investment. Technology-related expenses include evolving and adapting reference software components (such as participant agents (connectors), identity and access management mechanisms, and data catalogues), designing the dataspace, and deploying it in a public or private cloud infrastructure. To justify these costs, a compelling value proposition for each data space participant (consumers or providers) is crucial.
The primary aim of the service is to assist the customer in designing well-defined dataspace use cases. The service offers aid in the definition of the purpose, review of the participant roles (that could be data provider, data consumer, data intermediary, or part of the data space governance authority), business and governance models outline, and specification of the reference architecture and user interface of the dataspace.
Tools used in this process include the Starter Kit for Data Space Designers [1], Data Cooperation Canvas [2], Use Case Playbook [3], and Use Case Blueprint [4].

[1] https://dssc.eu/download/attachments/29523973/DSSC-Starterkit-Version-1.0.pdf?download=true
[2] https://www.datacooperationcanvas.eu/canvas/intro
[3] https://internationaldataspaces.org/wp-content/uploads/dlm_uploads/use-case-playbook.pdf
[4] https://dataspacessupportcentre.atlassian.net/wiki/spaces/BVE/pages/357074241/Use+Case+Development 

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.

Design of test environments for physical testing

Politecnico di Milano (POLIMI)

Università degli Studi di Milano (UMIL)

University of Cordoba (UCO)

Universitat de Lleida (UdL)

Location

Italy

Spain

Remote

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 AI-based optimal pesticide dosage in tree crops

Universitat de Lleida (UdL)

Location

At user's premises

Spain

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.

Evaluation of AI Camera Perception Systems

INRAE

Location

France

Arable farming

Greenhouse

Horticulture

Livestock farming

Tree Crops

Viticulture

The Evaluation of AI Camera Perception Systems service offers a holistic approach to test AI-based monocular RGB camera perception systems. It provides the following tasks:Record and annotate RGB images by the operational domain (vineyard, orchard, or other crop); create relevant (for your application) datasets covering a large number of scenarios (variation in parameters concerning the vegetation, the meteorological conditions, and humans' position and posture in the scene); evaluate the systems (hardware or software/algorithm) to datasets and analyse responses. This service provides a complete solution for developing, refining, and evaluating perception systems based on cameras using AI algorithms in agricultural and other operational environments.

Evaluation of AI performance based on mixed testing environments

Laboratoire National de Meterologie et d'Essais (LNE)

Location

France

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 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

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.