Catalogue of Services

Assessment of AI algorithm performance

Fondazione Bruno Kessler (FBK)

Location

Remote

Arable farming

Greenhouse

Horticulture

Livestock farming

Tree Crops

Support clients in defining metrics and testing their AI solutions in real life, in the field, or virtually. Within normal and "stressed" operating conditions, including AI-generated test cases, we assess the readiness of agriculture-related AI algorithms for classification (weeds, chromatic analysis of crops), surround understanding and perception capabilities (for safety, for navigation), yield measurement (quantity of produce, quality), and robotics (anomaly detection, etc.). Besides accuracy metrics, we have experience evaluating memory and computing power footprints, and by doing so, understanding bottlenecks and improving the energy efficiency of proposed solutions.

Assessment of AI performance and software optimisation on edge computing devices

Fondazione Bruno Kessler (FBK)

Location

At user's premises

Italy

Remote

Arable farming

Food processing

Greenhouse

Horticulture

Livestock farming

Tree Crops

Viticulture

The tests in this service are aimed at products that run complex apps (based on a multitude of microservices, including heavy AI algorithms) in the fields, with limited to no connectivity to the cloud. In these cases, we propose a benchmarking designed to understand what the bottlenecks are in the implemented solution architecture to help TEF customers improve the efficiency and reliability of such products/solutions. The service may also encompass virtual facility elements (enriched datasets, virtual machines hosting containerised modules) blended with physical sensors and infrastructure in our test field or directly installed at the client's premises if need be for the duration of the experimentation.

Assessment of dataset quality

Laboratoire National de Meterologie et d'Essais (LNE)

Location

Remote

Arable farming

Food processing

Greenhouse

Horticulture

Livestock farming

Tree Crops

Viticulture

With the development of AI models in most sectors of society, the importance of the data used to train and test these models has been an important concern for the AI supplier. Understanding the quality of the datasets used, especially in sectors such as the agricultural and agrifood domains where data can be rare, is thus a prerequisite to ensure the correct development of AI models and build trust regarding these approaches for society. This service aims to answer this challenge by assessing the quality of datasets provided by the client. The features of the datasets that will be analyzed are based on agreed definition by the AI community: completeness, balance, diversity, accuracy... The dataset's quality assessment will begin by establishing the list of the system's influencing factors necessary for the constitution of a database. These factors will be identified both by using LNE expertise and outside sources, such as agrifood-relevant experts. Using these factors, LNE will analyse the distribution (or weight) of the various influencing factors in the databases and compare it to that under normal conditions of use in order to assess the representativity and balance of the dataset.

Assessment of interoperability for AI-driven solutions

Wageningen University WUR

Location

At user's premises

Netherlands

Remote

Arable farming

Greenhouse

Horticulture

Livestock farming

Tree Crops

Viticulture

Data interoperability in the agrifood sector hinders innovation and development due to the need for many custom solutions to share data. This service helps agricultural organisations improve how they handle and share data across the entire food value chain. By implementing standardised reference data models like rmAgro (https://rmagro.org), we help to optimise your data flows and make your information systems work better together. Our team at Wageningen Research provides expert guidance in data modelling and implements reference models that align with industry standards and tests the reference models against various use cases. This promotes more efficient data sharing between different systems and organisations, reducing data integration challenges and improving operational efficiency. The service is particularly valuable for organisations looking to modernise their data infrastructure or needing to share data more effectively with partners in the agri-food sector. This service provides an assessment of interoperability for AI-driven solutions within the agri-food sector. The service facilitates conformance testing and verification of whether the related IT systems comply with relevant standards, guidelines, data space regulations, and other interoperability requirements. By evaluating the IT systems against established reference data models and frameworks like rmAgro (https://rmagro.org), the service ensures that it meets the necessary criteria for effective data sharing and integration. Wageningen Research leverages its expertise to evaluate the overall AI solution on its quality, performance, and how well it aligns with industry standards, offering insights and recommendations for improvement. This service supports organisations in ensuring their solutions are interoperable, compliant, and ready for seamless integration within the agri-food value chain.

Assessment of the feasibility of integrating payloads or equipment into UGVs

Gradiant Technologies

Location

Remote

Spain

Arable farming

Greenhouse

Horticulture

Livestock farming

Tree Crops

Viticulture

This service will evaluate the feasibility of securely integrating third-party equipment or payloads related to the agrifood sector into unmanned ground vehicles (UGVs). The goal is to assess whether such integrations can be done safely and efficiently, ensuring that the added equipment does not interfere with the performance and operation of the UGV. This service will place special focus on key integration concepts such as minimum functional requirements, communication interfaces, power supply for equipment, physical integration of devices, and software compatibility. The outcome will include an evaluation of the feasibility of the integration, along with possible recommendations for the suitable configuration to ensure the proper functioning of the system.

Assessment of the feasibility of integrating payloads or equipment into UAVs

Gradiant Technologies

Location

Remote

Spain

Arable farming

Horticulture

Livestock farming

Tree Crops

Viticulture

This service will evaluate the feasibility of securely integrating third-party equipment or payloads related to the agrifood sector into unmanned aerial vehicles (UAVs). The goal is to assess whether such integrations can be done safely and efficiently, ensuring that the added equipment does not interfere with the performance and operation of the UAV. This service will place special focus on key integration concepts such as minimum functional requirements, communication interfaces, power supply for equipment, physical integration of devices, and software compatibility. The outcome will include an evaluation of the feasibility of the integration, along with possible recommendations for the suitable configuration to ensure the proper functioning of the system.

Assistance for Identifying AI Technologies for Agricultural Robotics, Drones, and Precision Machinery

Danish Technological Institute (DTI)

Location

Denmark

Remote

Arable farming

Food processing

Greenhouse

Horticulture

Our service is tailored to support AI technology providers, particularly in robotics and drone sectors, focusing on agricultural applications. We identify AI technologies that boost agricultural efficiency and productivity and offer integration support for embedding AI into agricultural robotics, drones, and precision machinery. Our guidance includes selecting effective AI tools and algorithms for specific agricultural challenges. Additionally, we offer comprehensive screening of AI technologies for agricultural relevance, which supports the company’s seamless integration of AI into existing equipment.

Business Model and Market Development Support for Robotic and AI Solutions in Livestock Farming

Wageningen University WUR

Location

Netherlands

Remote

Livestock farming

WUR and Agro-Innovation Centre De Marke have extensive knowledge of the dairy market developments in the Netherlands and Western Europe. They are able to advise customers of these market trends, can do specific market research, and can support doing a proper business model analysis for the specific knowledge. We have dedicated business developers and a large group of specialists on the team. Additionally, the large farmers network of Agro-Innovation Centre De Marke can be used to organise feedback sessions with end-users and their dealers. 

Calibration and Optimisation of Technological Quality Measurement Methods for Cereal Grain

ARVALIS

Location

France

Arable farming

Our service provides access to grain samples and comprehensive technological quality analysis conducted at Arvalis facilities, enabling the development of AI-powered grain analysis solutions. Clients can work with well-documented samples from one or multiple species, enriched with detailed metadata such as variety, harvest year, and collection location crucial for training and validating AI models. Beyond sample selection and preparation, our experts assist in choosing, testing, and validating analytical methods, covering rheological properties (Alvéolab), breadmaking tests, and protein content measurement (Infratec, Dumas). These high-quality datasets, combined with access to Arvalis facilities and controlled testing environments, provide an ideal foundation for developing machine learning algorithms that enhance grain quality prediction, automate classification, and optimise processing parameters. With our service, clients can accelerate the development, validation, and deployment of AI-driven grain analysis tools, ensuring they meet industry standards and deliver precise, reproducible results. Our expertise in analytical methods allows customers to refine their models, improve prediction accuracy, and scale AI solutions for real-world agricultural applications.

Co-innovation, test and support on sensors and practical robots for livestock farming

Wageningen University WUR

Location

Netherlands

Remote

Livestock farming

Provision of practical and scientifically based input and collaboration in designing robotic AI systems and components for livestock farming solutions.
 

Receive practical and scientific input for the (re)design of sensors and robotics; help with decision support on dairy farms that meet agro-ecological F2F criteria, located in Hengelo Gld. and Leeuwarden.
Access to high-end end-users in a country takes lots of time and effort. Thanks to the multiple regional experimental farms, Wageningen Research has a wide and easily accessible farm and dealer network. Additionally, researchers at Wageningen Livestock Research have a practical approach and know what's needed and required to implement new technologies in farms, therefore fulfilling a bridge between agronomy, technology, and ecology. By testing and demonstrating the products or services to our farm managers and farm networks in specific regions, we can gather valuable feedback and help speed up the developmental stage of the tested prototypes. 

Collection of test data during digital testing

Politecnico di Milano (POLIMI)

Location

Italy

Remote

Arable farming

Food processing

Greenhouse

Horticulture

Livestock farming

Tree Crops

Viticulture

One of the key activities during digital testing is the collection of data concerning the progression and the final outcome of the tests. Such data enable the evaluation of system performance by the customer or – if needed – by AgrifoodTEF (via Service S00184). This service manages the collection of data relevant to performance evaluation produced during the tests by both the system under test and the computational environment where the tests take place. 

Examples of collected data comprise information produced within a virtual environment to simulate sensor data collection in a physical environment; statistics about AI model performance in the test and deployment phase (e.g., occupied memory, number of trainable parameters, training/optimisation loss, etc.); specific labels and annotations to use as ground truth for evaluating the system; and system output when subjected to a range of test conditions. The minimum set of data to be collected is defined by the evaluation metrics that the user chose (either on their own or with AgrifoodTEF support, via Service S00178) to process them; generally, a larger set of data wrt the minimum is selected by AgrifoodTEF together with the customer to provide a richer view of the system’s performance and to enable the application of other metrics in the future, if needed. As an output of the service, in addition to the raw data, we also provide the customer with documentation describing logged features and conditions of the testing environment at the time of testing, as well as any parameter values, variation ranges and specifics required for reproducibility purposes.

Computer vision for automated food quality assessments

Universitat de Lleida (UdL)

Location

Remote

Spain

Food processing

Validation of an existing automated food quality assessment solution based on Artificial Intelligence, in particular Computer Vision techniques. The service will help the solution provider check its performance using a controlled testing infrastructure, including our Food Processing Pilot Plant. The service might also include the generation of reference datasets of food samples and expected quality measures, which can be used both for validation and training of the Artificial Intelligence solution based on Computer Vision.