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Evaluation of AI performance based on mixed testing environments

Laboratoire National de Meterologie et d'Essais (LNE)

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.

Performance evaluation

Test design

Test execution

Test setup

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

Lukasiewicz Poznanski Instytut Technologiczny (L-PIT)

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

Performance evaluation

Test design

Test execution

Test setup

Evaluation of AI solutions performance based on testing datasets

Laboratoire National de Meterologie et d'Essais (LNE)

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.

Collection of test data

Performance evaluation

Test design

Test execution

Test setup

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

University of Cordoba (UCO)

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.

Collection of test data

Data analysis

Performance evaluation

Test execution

Evaluation of Crop Protection Equipment

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

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.

Collection of test data

Desk assessment

Performance evaluation

Test design

Test execution

Test setup

Evaluation of ethical aspects in agrifood technology design and development

Universitat de Lleida (UdL)

We offer a specialised service to companies, particularly small and medium-sized enterprises (SMEs), that develop AI- and robotics-based products and services for the agri-food sector. Our service supports these companies in integrating ethical considerations throughout the design and development process of their AI- or robotics-based products and services. By utilising Value Sensitive Design (VSD) methods, we ensure that human values such as sustainability, fairness, transparency, and privacy are incorporated into technological solutions from the outset. Through stakeholder analysis, participatory design, value scenarios, and ethical reflection, we guide companies in creating responsible, innovative technologies that align with societal needs and ethical standards.

Desk assessment

ELSA assessment

Test design

Evaluation of Precision Herbicide Application Systems

Universitat de Lleida (UdL)

This service tests the performance, efficacy and efficiency of herbicide application systems that use AI and robotics to optimise spray treatments, including spot spraying booms and variable-rate applicators. It evaluates the system’s ability to apply herbicides accurately, reducing usage while maintaining effective weed control. Through controlled field experiments and real-farm testing, we assess herbicide savings, weed control effectiveness, crop yield impact and environmental benefits compared to conventional broadcast spraying methods. The service also validates the system's adaptability to different environmental conditions, such as varying soil types, weather, and crop growth stages, and it is applicable to herbaceous and arable crops, offering valuable insights for improving targeted weed control and reducing unnecessary herbicide use.

Collection of test data

Performance evaluation

Provision of datasets

Test design

Test execution

Test setup

Evaluation of readiness for testing

RISE - Research Institutes of Sweden

Customers are often unaware of the best way to conduct testing and may require support and coaching to reach a state of test-readiness and proceed to purchase a testing service from TEF.
Service S00248 is designed to help customers understand and navigate the various steps involved in testing. The service offers clear and straightforward guidance to ensure customers are fully prepared for testing. By collaborating with RISE experts, customers can be confident that they are receiving the appropriate advice to achieve their goals.
The aim of the service is to make the testing process more accessible and understandable, allowing customers to focus on their core activities.

Desk assessment

Evaluation of readiness for testing of digital systems or data

Politecnico di Milano (POLIMI)

Università degli Studi di Milano (UMIL)

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

Evaluation of readiness for testing of physical systems

Politecnico di Milano (POLIMI)

Università degli Studi di Milano (UMIL)

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

Evaluation of results of digital testing

Politecnico di Milano (POLIMI)

Università degli Studi di Milano (UMIL)

This service concerns the processing of data collected during digital testing of a system, i.e., experimentation done in a computational environment. Digital testing enables easy collection of experimental data; however, such data then need to be suitably processed to extract information about system performance.

The service applies a predefined set of performance metrics (defined either by the customer or via service S00178) to the collected data. Data collection during test execution is expected to have been done by the customer (on request, this activity can be supported by agrifoodTEF via service S00183). The service includes all the activities needed to apply the performance metrics, including the development of any software component needed for data processing or data preparation.

The output of the service is a report providing the quantitative results of the application of the performance metrics to the experimental data. The report also includes an evaluation of these results aimed at highlighting possible issues and pointing out lines for improvement.

Performance evaluation

Evaluation of results of physical testing

Politecnico di Milano (POLIMI)

Università degli Studi di Milano (UMIL)

This service performs the crucial step that follows the execution of physical experimentation, i.e., evaluation of the performance of the system under test. This activity requires the application of suitable performance metrics to specific test data collected during the tests and then interpreting the results in view of the features of the system and the experimental setting. Depending on the specific use case, performance metrics can involve pure computer processing, human expertise by agronomists, or a combination of both.

Application of performance metrics may include the development of custom software to extract the necessary information from experimental data and/or to apply suitable processing to the information. If the performance metrics require the data to be subjected to some form of pre-processing, Service S00115 may be leveraged to prepare the data.

Beside the object of this service (i.e., evaluation of results), agrifoodTEF can, on request, support the customer along any other aspect and phase of the physical experimentation pipeline. For instance, for the design of the environment and protocol for the test, the customer can leverage services S00106 and S00107. Preparation of the test environment can be done either by the customer or by agrifoodTEF via service S00110; in both cases, assistance in interconnecting the system under test with agrifoodTEF’s test infrastructure is available via service S00111. Finally, data collection during the test can, if needed, be provided by agrifoodTEF via service S00113.

Data analysis

Performance evaluation

Catalogue of Services

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