Catalogue of Services

In order to train, test, and validate AI applications, annotated datasets are needed. Wageningen Research - Field Crops can provide FAIR datasets consisting of high-quality raw images, annotations, and metadata on crops, weeds, diseases, and pests. At the moment a dataset on volunteer potato in sugar beet and onion is available. Let us know in which other datasets you are interested.

Collection of test data

Data augmentation

Assessment of interoperability for AI-driven solutions

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.

Conformity assessment

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

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.

Desk assessment

People training

Business Model and Market Development Support for Robotic and AI Solutions in 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.

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

People training

Test design

Custom AI-Optimized Testing Protocol Design for Agricultural Solutions

We specialise in designing comprehensive testing protocols for the physical testing of customer solutions in the agricultural sector. Our approach focuses on creating consistent procedures that enhance the precision of AI models developed from the collected data. These protocols are carefully crafted to meet individual customer and project needs, incorporating requirements for both laboratory and field testing scenarios. We prioritise automation in data collection to ensure consistent and repeatable testing procedures, which is crucial for high-quality data collection. This service bridges the gap between physical testing and AI model development, providing a solid foundation for advancing agricultural technologies.

Test design

Test setup

Data analysis support for specific and long-term data sets for arable and livestock farming data sets

WUR experts are available for analysis or advice to extract information from large and multiple-source data sets as provided with the service “Provision of specific and long-term data sets for arable and livestock farming data from the Farm of the Future experimental farms (S00132).”
It is also possible to analyse datasets of clients that are used for the development of sensors, AI, and robotic solutions.

Data Collection for Advanced Agricultural Environmental Monitoring and Analysis Systems

We offer cutting-edge technical systems, both hardware and software, along with expert knowledge for collecting and analysing relevant environmental data during agricultural testing. Our service emphasises the use of standardised protocols to ensure enhanced accuracy and efficiency. We provide a comprehensive suite of monitoring and analysis tools, including methane detection systems, PTR-MS for volatile organic compound analysis, GHG measurement using CRDS technology, and automated climate data collection using IoT sensors and AI algorithms. These advanced systems enable precise environmental monitoring and data analysis for agricultural research and development.

Collection of test data

Cybersecurity

Data augmentation

Test design

Test execution

Test setup

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

Politecnico di Milano (POLIMI)

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 results of physical testing

Università degli Studi di Milano

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. Besides 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.

Life Cycle Assessment (LCA) for Agriculture Robots

Life Cycle Assessment (LCA) for Agriculture Robots is a comprehensive evaluation method that assesses environmental impacts at every stage of a robot’s lifecycle—starting from raw material extraction, manufacturing, utilization, and ending with disposal or recycling. This service provides valuable insights into energy consumption, waste generation, and other environmental factors, helping customers identify key areas for improvement. By promoting sustainability and eco-efficiency, LCA supports decision-making for reducing ecological footprints and ensuring compliance with sustainability standards such as Frugal AI requirements. The service is tailored to meet diverse customer needs, enabling them to understand and optimize the environmental performance of their robotic technologies.

LCA assessment

Provision of a phenotyping greenhouse with extensive sensor set - NPEC

NPEC will enable you to make accurate, high-throughput studies of plant performance possible in relation to relevant biotic (diseases, bacteria) and abiotic (light quantity and quality, nutrients, temperature, moisture, soil pH and atmospheric CO2 level) factors across a range of scales.

The experiments can be performed in a greenhouse compartment with an extensive set of sensors to measure the plant and it’s environment. In growth rooms smaller scale experiments can be performed, with a special chamber for studying the roots of plants using transparent growth media. In the open field crop measurements can be performed using the Traitseeker for RGB, spectral and 3D measurements.

Collection of test data