Catalogue of Services

Design of testing protocols for digital testing

Politecnico di Milano (POLIMI)

Università degli Studi di Milano (UMIL)

Location

Italy

Remote

Arable farming

Food processing

Greenhouse

Horticulture

Livestock farming

Tree Crops

Viticulture

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.

Design of testing protocols for physical testing

Politecnico di Milano (POLIMI)

Università degli Studi di Milano (UMIL)

Location

Italy

Remote

Arable farming

Food processing

Greenhouse

Horticulture

Livestock farming

Tree Crops

Viticulture

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.

Desk assessment of the proposed solution

Fondazione Bruno Kessler (FBK)

Location

Remote

Arable farming

Greenhouse

Horticulture

Livestock farming

Tree Crops

Viticulture

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.

Evaluating autonomation readiness of agricultural robots

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

Location

At user's premises

Belgium

Arable farming

Horticulture

Tree Crops

Viticulture

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.

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 for Ethical, Legal, and Social Aspects (ELSA Scan) for responsible AI development

Wageningen University WUR

Location

At user's premises

Netherlands

Remote

Arable farming

Food processing

Greenhouse

Horticulture

Livestock farming

Tree Crops

Viticulture

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. 

Evaluation of AI model for agrifood applications

GRADIANT

Location

Remote

Arable farming

Food processing

Greenhouse

Horticulture

Livestock farming

Tree Crops

Viticulture

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.

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 AI solutions for crops, livestock, and food products analysis

Lukasiewicz Poznanski Instytut Technologiczny (L-PIT)

Location

Poland

Remote

Arable farming

Food processing

Greenhouse

Horticulture

Livestock farming

Tree Crops

Viticulture

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.

Evaluation of AI solutions performance based on testing datasets

Laboratoire National de Meterologie et d'Essais (LNE)

Location

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 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 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 Crop Protection Equipment

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

Location

Belgium

Arable farming

Greenhouse

Horticulture

Tree Crops

Viticulture

This service assesses the performance of advanced precision crop protection equipment, including map- and sensor-based precision spraying, non-chemical weed and disease control, and robotic applications. Evaluation is conducted both in controlled laboratory settings and real-world field conditions to assess and quantify efficacy, accuracy, and efficiency. The ILVO Spray Tech Lab, one of the world's few ISO 17025-accredited spray technology laboratories, specialises in characterising sprayers and spray nozzles. Additionally, we provide field testing for sensor-based precision spraying technologies and non-chemical solutions (e.g., mechanical, laser, and UV-based weed and disease control). For robotic applications, our robotic framework can be used, ensuring practical deployment feasibility in autonomous precision agriculture applications. The results help manufacturers and farmers optimise their equipment for more automated and effective crop protection tools.