Catalogue of Services

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 activities for digital systems or data

Politecnico di Milano (POLIMI)

Università degli Studi di Milano (UMIL)

Location

Italy

Remote

Arable farming

Food processing

Greenhouse

Horticulture

Livestock farming

Tree Crops

Viticulture

Through this service we offer customers a complete feasibility study of their digital systems and/or data, evaluating their suitability for the test use cases identified by the customer. The study involves both technical and agronomic aspects. The goal of this service is to provide the customer with a comparison between the features of the system or data submitted for review and the requirements of the use cases chosen for (possible) subsequent testing.

Examples of systems and data requiring digital testing include (but are not limited to) software modules for data processing (e.g., machine vision modules), AI models, software architectures (e.g., control system of a robot), simulations, datasets collected in the field, synthetic datasets, designs of software systems, and so on.

This assessment is crucial to provide customers with key information on elements of their system that need to be further developed to become “fit for testing”. It can also identify changes to apply to either the system or agrifoodTEF’s testing facilities to meet the requirements for experimentation.

Desk assessment activities for physical systems

Politecnico di Milano (POLIMI)

Università degli Studi di Milano (UMIL)

Location

Italy

Remote

Arable farming

Greenhouse

Horticulture

Tree Crops

Viticulture

Through this assessment, we offer customers a complete feasibility study of the use cases and systems that customers would like to test, comprising both technical and agronomic aspects. Examples of systems requiring physical testing include (but are not limited to) precision sprayers, weeding machines, robotised fruit picking systems, and other implements; examples of subsystems include (but are not limited to) perception systems (e.g., based on vision), autonomous driving modules, localisation systems, and mapping and navigation subsystems.

This assessment is crucial to provide customers with key information on elements of the system that need to be further developed to become “fit for testing”. It can also identify changes to apply to either the system or AgrifoodTEF’s testing facilities to meet the test requirements.  

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.

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-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 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 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 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.