Catalogue of Services

Filtered results

Sort by:

Relevance Title

Hyperspectral and Spectrometer Testing for Agri-Food Product Quantification

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

This service offers pilot testing of hyperspectral and spectrometer technologies to evaluate their potential for non-destructive, at- or inline analysis in agri-food processing.

Using VIS and NIR hyperspectral cameras and spectrometers, we conduct lab-scale measurements to assess the feasibility of these technologies for your specific products or applications, such as quality control or process monitoring. Based on the collected data and reference samples, we build statistical and machine learning-based chemometric models to quantify key properties.

Additionally, we offer resampling services to explore the integration of multispectral devices, whether existing or custom-built, into your production processes for optimized performance.

Collection of test data

Performance evaluation

Provision of datasets

Test design

Test execution

Test setup

Livestock Dataset Generation

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

Location

Belgium

Livestock farming

This service supports the generation of high-quality datasets tailored for validating and improving AI-driven technologies or sensors designed for livestock management. Whether you are developing new tools for monitoring, controlling, or working with livestock, we help collect and process relevant data that reflects real-world conditions. These datasets enable you to fine-tune your AI models and sensor systems, ensuring they operate accurately and effectively. By leveraging this service, you can optimise your solutions for livestock health monitoring, behaviour analysis, and automated control systems in the agri-food sector.

Collection of test data

Desk assessment

Performance evaluation

Provision of datasets

Test design

Test execution

Test setup

Policy lab for innovative drone technologies in regulatory sandboxes

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

This service is designed to support agri-food companies that want to test and evaluate the use of innovative drone technologies in a controlled environment—a so-called regulatory sandbox. Setting up such a drone sandbox requires navigating complex airspace regulations, flight safety rules, environmental constraints (e.g., drift, pesticide use), and data protection standards. Our experts help you design and prepare a sandbox operation from A to Z: from selecting a test location and identifying legal permissions needed to preparing risk assessments, notifying authorities, and ensuring safe operational procedures. We also offer advice on sensor integration, traceability, and aligning with EU drone and agricultural regulations such as Regulation EU 2019/947 on the operation of unmanned aircraft. This is an ideal service for companies wishing to run trials with crop spraying or precision seeding drones before launching commercially. - Set up a regulatory sandbox to safely and legally test spraying or seeding drones. - Understand the full list of permissions, safety protocols, and risk assessments required. - Learn how to engage with authorities (e.g., airspace regulators, pesticide control bodies). - Get support in preparing the documentation to operate drones legally during the test phase.

Desk assessment

Test design

Test setup

Provision of a phenotyping greenhouse with extensive sensor set - NPEC

Wageningen University WUR

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

Performance evaluation

Provision of datasets

Test execution

Test setup

Provision of a virtual playground for robots

Wageningen University WUR

The service consists of a photorealistic digital greenhouse environment, built in a game engine.

The greenhouse is configurable to different sizes, light conditions (sun, supplementary lighting) or row spacing. In the greenhouse different crops can be placed to either test AI detection networks or complete robotic systems, as there is a ROS2 integration available. The environment is made in a way that physical properties of the objects have influence on other components or robot interactions.

AI model training

Collection of test data

Data augmentation

Performance evaluation

Provision of datasets

Test design

Test execution

Test setup

Provision of datasets for simulation and visualisation

Wageningen University WUR

In order to develop digital twins for agriculture, reliable long-term datasets on the agronomy and ecology of fields are crucial. From several long-term experiments and agricultural systems (like the Farm of the Future), datasets of fields are available with registrations of crop operations, crop measurements (e.g., timeseries of vegetation indices), yield, soil, & weather data, and data on key performance indicators. In some cases, other sensor data (tractor data, soil moisture, insect monitoring) are also available. New datasets can be developed based on specific needs.

Provision of datasets

Provision of datasets for validation of AI algorithms for livestock farming

Wageningen University WUR

Thanks to 30 years of data gathering at Agro-innovation Centre De Marke, multiple data sets are available. During these years, data is gathered on individual cow events, barn emissions, yields of specific and typical livestock farm crops, soil samples were taken, and feeding strategies were developed. Due to the systematic approach of De Marke, this data can be easily reused for setting up specific validation of AI algorithms. Additionally, new data can be gathered as requested.

Collection of test data

Provision of datasets

Provision of raw image datasets in field crops

Wageningen University WUR

In order to develop AI applications or vision functionalities in robots, one of the first steps is to have high-quality images of crops, weeds, nutrient deficiencies, diseases, or pests available. As the phenotype of plants can really differ between regions and seasons, providing balanced datasets with high-quality images and different illumination could save valuable (development and acquisition) time. The data can be used for annotation purposes or to have a first visual check if an existing trained algorithm works sufficiently.

Collection of test data

Provision of datasets

Test and demonstration of (robotic sensor, implement, algorithm) solutions with end-users

Wageningen University WUR

Accessing the end-user in a country takes a lot of time and effort. Wageningen Research - Field Crops has, through its regional experimental farms, access to farmer networks and dealers and can bring you in contact with them. The researchers in our department have a practical approach and know what is needed to implement new technologies in farms, therefore fulfilling a bridge between agronomy, technology, and ecology. By testing and demonstrating your product or service to our farm managers and farmers in the region (for example, farmers in https://www.proeftuinprecisielandbouw.nl), we gather valuable feedback to speed up the development of your prototype.

Market research

Test execution

Test and experimentation of robotic and AI solutions in field crops

Wageningen University WUR

The core business of Wageningen Research - Field Crops is to design and create experimental plots (e.g. at the Farm of the Future) and to collect objective data on crops, weeds, diseases, pests and quality and effectiveness of robotic and AI solutions, in which we provide statistical reliable datasets. Examples of experiments are e.g. testing the accuracy and quality of handsfree harvesting solutions, weeding robots and path following of autonomous implement carriers in field crops. We offer for example the following measurements : -Provide measurements on effectiveness of solutions on control of weeds, pests and diseases (e.g. effectiveness of a weed robot) -Provide timeseries of vegetation indices (drone) -Provide yield measurements (quantity and quality) -Provide plant measurements (emergence, flowering, senescence) -Provide weed countings (including species determination) -Provide sensor data (soil moisture, insect monitoring, etc.) Furthermore we have common agricultural machines and implements available. We can cover different field crops, but we also are specialized in new crop farming systems that are based on agro-ecological principles. Think of strip and mosaic farming. Get in touch to discuss the possibilities for your experiment or test question.

Collection of test data

Performance evaluation

Provision of datasets

Test design

Test execution

Test setup

Test, validation, and data collection of robotic and AI systems and components for livestock solutions

Wageningen University WUR

Location

Netherlands

Livestock farming

In the setting of the Farms of the Future (De Marke), we can (physically) test and validate robotic and AI solutions that are dedicated to dairy solutions. In this context we are also able to collect appropriate data sets of these tests and validation experiments. Test and validation data will be compared to ground truth data sets.

Collection of test data

Desk assessment

Performance evaluation

Test design

Test execution

Test setup

Testing and evaluation of innovative drone technologies in regulatory sandboxes

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

AgrifoodTEF provides access to realistic test environments where innovative drone technologies can be evaluated under controlled yet practical conditions. This includes technical validation of AI systems, embedded sensors, software, and robotic controls used during operations. The tests are adapted to the specific objectives and constraints of a regulatory sandbox, helping companies explore system performance, robustness, and safety in a near-to-real-world configuration. The feedback and technical documentation generated during testing can be used to refine the product and support regulatory approval, certification, and future market deployment.

AI model training

Collection of test data

Conformity assessment

Data analysis

Desk assessment

Performance evaluation

Provision of datasets

Test design

Test execution

Test setup