Catalogue of Services

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

Data augmentation

Test design

Politecnico di Milano (POLIMI)

Provision of datasets

Università degli Studi di Milano (UMIL)

During the development of systems based on AI and/or robotics, tailored data are needed to support the realisation, validation and optimisation of systems and subsystems. This service aims at providing such data.

AI models are a noteworthy example of systems that require data to be trained; more generally, any system which processes data produced via artificial perception (such as images, videos, LiDAR, ultrasound, radar...) requires suitable data to be built, tested and fine-tuned.

This service allows a customer to define, together with agrifoodTEF, the specific features of the data that the customer’s activities require and to receive datasets precisely matching such features. Among the features to be defined are the type, number and specifications of the data streams comprised in the datasets (e.g., when multi-sensor data are required) and the range of variations encompassed by the data (e.g., the variety of environmental conditions represented in a sensor stream); lower-level features to be defined concern the formatting and packaging of the data.

Provision of datasets for simulation and visualisation

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 for validation of AI algorithms for livestock farming

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.

Provision of raw image datasets in field crops

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.

Politecnico di Milano (POLIMI)

Support in interconnecting systems to physical infrastructure

Università degli Studi di Milano (UMIL)

Testing a system in a physical environment requires an environment that has been prepared and set up to support the specific testing activities to be executed. Such an environment usually involves both agronomy-related elements (machines, fields, greenhouses, cultivations...) and technical infrastructure (sensors, networks, software, logging systems...). In order to execute the testing, the physical system under test (e.g., an agricultural robot) must correctly interface with all these elements; this service takes care of ensuring this.

For instance, it is crucial to ensure that the communication between the system under test and the technical infrastructure supporting the testing activities is correct, smooth and uninterrupted.

Wherever needed, the service also includes the development of custom components needed to ensure a successful two-way integration between the system and the testing infrastructure (e.g., a software component to format data or perform transcoding).

While available separately, this service is often used in association with service S00110 (Preparation of physical test environment).

Politecnico di Milano (POLIMI)

Support in interconnecting systems to AgrifoodTEF’s computational test infrastructure

Università degli Studi di Milano (UMIL)

Digital testing requires interaction among several different components which have to reliably cooperate to enable the testing activities and to ensure that their outcomes reliably reflect the system’s capabilities. When digital testing of customer systems leverages agrifoodTEF’s computational infrastructure, integration between the infrastructure and the systems to be tested is needed before the tests/experiments can be executed. If the customer does not possess the technical expertise – or does not want to devote resources – to perform the integration, this service can support them in the process. Help is provided by a team of expert engineers.

This service concerns the integration of the customers’ system and/or data used for testing with the agrifoodTEF digital testing infrastructure. In particular, integration is crucial to ensure smooth and uninterrupted communication between the system and the infrastructure that produces, collects and/or processes test data.

This service also includes the development of any custom components needed to ensure a two-way integration between the system and the reference infrastructure (e.g., software components to format data or to perform transcoding).

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

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 and experimentation of robotic and AI solutions in field crops

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.

Test design

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

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.

Desk assessment

Test design

Validation of robotic systems and algorithms

End-users like to see an objective validation of a technology in terms of added value (costs vs. benefits), technical specifications (accuracy, capacity, power requirements, etc.), and user acceptance (ease of use, gain, and joy of working with the technology). At Wageningen Research - Field Crops, we can validate your solution according to the agrifoodTEF protocols and deliver you a WUR white report on your technology.