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.

AI model training

Collection of test data

Data augmentation

Assessment of agricultural potential for emerging technologies in AI & robotics

Our service is designed to assist clients in identifying and evaluating potential agricultural applications for AI or robotics technologies originally developed for other sectors. Clients can provide detailed information about their technology, including its current applications and technical specifications. Our expert team will conduct extensive research to identify possible use cases related to the agricultural and food production sector, analysing factors such as feasibility, potential benefits, and relevance to current agricultural challenges. The tools used for this work will depend on the use case and may include, for example, online research as well as expert interviews. The customer will receive the results as a comprehensive report outlining the identified potential applications, adoptions needed and an assessment of their relevance and impact within the agricultural sector. This service enables clients to explore new markets and adapt their technologies to meet the evolving needs of modern agriculture.

Desk assessment

Market research

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

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.

Data analysis

People training

Test design

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 analysis

Data augmentation to optimise AI model training and prevent the model from overfitting

In preparing image or sensor data, pre-processing is crucial. We apply techniques like normalisation for data uniformity and correlation analysis to uncover patterns, enhancing interpretability. Dimension reduction optimises computational efficiency, while adding randomised noise and artificial expansion improves model generalisation. We implement anonymisation to protect data privacy and ensure precise data labelling for supervised learning. These methods, tailored to your needs, enhance your data's quality and usability.

Data augmentation

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

Instituut voor Landbouw-, Visserij- en Voedingsonderzoek

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

Field Spectrometer Measurements

This service utilises field spectrometers to measure the spectral properties of physical environments, such as barns, greenhouses, or outdoor fields. By capturing detailed spectral data, we help you develop, improve, or validate your agri-food solutions, whether for monitoring plant health, optimising lighting conditions, or enhancing environmental control systems. The measurements provide insights into how different wavelengths of light interact with the environment, enabling fine-tuned adjustments for better crop growth, energy efficiency, or precision farming applications. Additionally, this service can serve as a follow-up to lab-scale measurements where hyperspectral technology is used to conduct pilot tests to determine which wavelengths provide valuable information. In this service, we then perform field tests on these specific wavelengths.

Collection of test data

Conformity assessment

Desk assessment

Lukasiewicz - Poznanski Instytut Technologiczny

Functional safety testing of machines

Within the framework of this service, we offer the possibility of carrying out functional safety tests of objects in the form of, for example, autonomous vehicles, robots, machines, agricultural equipment and components, etc. The service provides opportunities to test these devices with measurements of their mechanical, physical, acoustic, electrical parameters and electromagnetic compatibility to assess the overall safety of the user of the tested vehicles, machines, equipment and components for agriculture, horticulture, forestry and food. Verification of the tested parameters is based on the requirements of standards and directives declared by the manufacturer as compatible with its product and general normative safety regulations. Initial evaluation of an object is carried out based on its provided design documentation and/or measurements made on a prototype based on harmonized and non-harmonized standards with relevant EU and sector regulations. In the case of autonomous vehicles, we also offer tests of the correctness of response and operation of safety devices built into their structures in standardized cases of hazardous situations. The results obtained can be used in the further process of product labelling, declaration of conformity to place the CE mark on the device.

Certification

Conformity assessment

ELSA assessment

LCA assessment

Test design

Test execution

Test setup

Performance evaluation of an AI model

Our service provides a thorough evaluation of AI models by utilising a wide range of benchmark datasets—from images and sensor data to remote sensing and multimodal data. This comprehensive testing assesses the model's performance across standard metrics, specifically chosen based on the model’s intended tasks. The results offer insights into the model’s effectiveness and accuracy, guiding developers in refining their AI solutions to achieve optimal performance in real-world applications.

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.

AI model training

Collection of test data

Data analysis