Catalogue of Services

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 test environments for physical testing

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.

Design of testing protocols for physical testing

Any physical testing activity comprises three main components:

the environment (where the tests are conducted),
the protocol (which defines the tests to be executed and their methodology),
and the evaluation metrics (used to assess the test results).

This service focuses on the protocol, while the environment and metrics can be designed as needed through services S00106 and S00108.

In the context of testing customer solutions within physical facilities, this service aims to create a suitable testing protocol based on the use cases specified by the customer. The components of the testing protocol defined in this phase may include:

Defining the different phases of the protocol and their duration
Outlining the operations to be executed in each phase
Quantifying the effort required for each phase, including the number and qualifications of personnel involved
Distributing testing operations over time, considering seasonal and daily variations, as well as weather conditions
Establishing acceptable and desired variation ranges for each configurable element in the testing environment
Determining the number of test repetitions required and their distribution across the variation ranges

To ensure a comprehensive approach to protocol design, this service involves a collaborative team of both engineers and agronomists.

Design of testing protocols for digital testing

Any test activity involves three main components, i.e., environment (where the tests take place), protocol (defining what activities are executed and how), and evaluation metrics (used to assess the results of the tests). This service concerns the second element, i.e., the design of the testing procedure for digital systems such as (for instance) AI models or computer vision software. The digital environment and the evaluation metrics can be designed – if required – via services S00176 and S00178.

In the context of testing customers’ solutions within digital environments, this service is targeted at designing a suitable protocol for digital testing based on the use cases specified by the customer. The components of the testing protocol can include:

Selecting the datasets to be used for testing
Selecting reference AI models to be used for testing (if needed)
Choosing data formats and metadata standards
Defining data pre-processing and preparation steps
Defining values and ranges of test parameters
Defining the different phases of the protocol
Outlining the operations to be executed in each phase
Thoroughly describing the protocol specifics to ensure reproducibility

Datasets to be used for testing can be provided by agrifoodTEF and/or by the customer; if nothing suitable is available, other agrifoodTEF services can be leveraged to collect and/or generate tailored data.

The technical team executing this service comprises expert engineers but can also involve agronomists when this is necessary to ensure the relevance of the tests for the use case, e.g., to determine the distribution of test repetitions across the variation ranges.

Fondazione Bruno Kessler (FBK)

Desk assessment of the proposed solution

This service consists of a detailed analysis of the customer's system, software, or AI solution. It is a preliminary step for testing and validation services, providing important information on the solution and leading the following activities. As a consequence, the detailed mapping of the solution can help the customer understand bottlenecks and better prepare the solution for testing and validation activities.

Desk assessment

Evaluating autonomation readiness of agricultural robots

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

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.

Collection of test data

Desk assessment

People training

University of Cordoba (UCO)

Evaluation and testing on the efficiency of SaaS platforms

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.

Collection of test data

Data analysis

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.

Evaluation of AI model for agrifood applications

This service helps businesses and organisations evaluate how well their AI models perform in real-world conditions. We conduct thorough testing of your AI model using either your own dataset or our reference datasets, depending on your needs (see the related services). Our evaluation process goes beyond basic accuracy metrics to provide a comprehensive understanding of your model's strengths and limitations. We analyse various aspects of performance that matter for your specific use case, whether it's detecting diseases in crops, sorting produce, or monitoring livestock behaviour. We work with you to determine the most relevant performance metrics for your application, taking into consideration factors like accuracy, speed, reliability, and resource usage. This helps you understand if your AI solution is ready for deployment, needs improvement, or requires adjustments for specific conditions. The evaluation provides clear, actionable insights about your model's performance, helping you make informed decisions about its readiness for real-world agricultural applications.

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.

Lukasiewicz Poznanski Instytut Technologiczny (L-PIT)

Evaluation of AI solutions for crops, livestock, and food products analysis

The service provides the testing, validation, and training of machine learning algorithms specialised in the detection of weeds, crops, livestock, and food products. The model data for the training implementation can come from different sources. They will come from services run by the Agrifood TEF service network. They may come from the service provider's implementation of other similar services, or they may be provided by the client of the service as collections resulting from their research. In this case, the datasets provided will be validated to assess their quality and suitability for machine training of artificial intelligence algorithms. The envisaged scope of the service, the algorithm training, relates to a wide range of fields and includes farming and plant breeding, animal husbandry, and food processing.

AI model training

Data analysis