Catalogue of Services

AI model training

Politecnico di Milano (POLIMI)

Università degli Studi di Milano (UMIL)

Location

Italy

Remote

Arable farming

Food processing

Greenhouse

Horticulture

Livestock farming

Tree Crops

Viticulture

This service concerns training AI models on behalf of the customer for a specific task and optimisation objective, e.g., improving accuracy on crop classification from image data.

The target model is the solution provided by the customer that needs to be enhanced with respect to a set of predetermined features to reach the desired performance level. However, if required, the training can also be applied to additional state-of-the-art models available in the market for benchmarking purposes.

If not defined by the customer, some features of the training process can be identified via service S00179 (desk assessment activities for digital systems and/or data): for instance, model features to improve, reference model baselines to include in the performance comparison, as well as benchmark datasets.

The data used for training the model can be either provided by the customer or annotated ad hoc as a preparatory activity to model training (via service S00290 – Data labelling); another possibility is that data are retrieved among reference benchmark datasets that are openly available. We will also agree with customers on the level of hardware acceleration required, based on the considered AI models, e.g., GPU acceleration via connection to a remote server vs on-device training.

AI model training for computer vision tasks in TEF infrastructure

GRADIANT

Location

Remote

Spain

Arable farming

Food processing

Greenhouse

Horticulture

Livestock farming

Tree Crops

Viticulture

The service allows training an AI model to be used for computer vision tasks, like object detection, segmentation, or classification tasks. The training is run on the TEF infrastructure, thus providing a powerful platform for developing cutting-edge artificial intelligence solutions in the agrifood sector. By leveraging high-performance computing resources, CPUs, and GPUs within the TEF infrastructure, this service enables innovators to train AI models using datasets based on images or videos. Users can either provide their own datasets or utilise existing data within the TEF infrastructure (see Related Services). The model training supports various architectures for the computer vision tasks, allowing the selection of the most appropriate one depending on the use case.

Annotated datasets for the validation of AI algorithms on crops, weeds, diseases, and pests

Wageningen University WUR

Location

Netherlands

Remote

Arable farming

Horticulture

Tree Crops

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.

Assessment of agricultural equipment connectivity through private 5G/NB-IoT network deployment

GRADIANT

Location

At user's premises

Spain

Arable farming

Food processing

Greenhouse

Horticulture

Livestock farming

Tree Crops

Viticulture

The service provides a comprehensive assessment of the connectivity of agricultural equipment, such as sensors and actuators, through private networks. By offering in-depth visibility into the network’s operation, it enables accurate troubleshooting, ensuring optimal connectivity for these kinds of devices. The service can handle the connection of multiple devices, making it suitable for validating the development of custom connectivity modules or testing agricultural equipment with built-in commercial modules. This allows for early detection of connectivity issues, fine-tuning module and network configurations, and ensuring smooth equipment operation. By deploying private networks, it is possible to implement configurations that simulate adverse conditions in deployment, such as successive outages, excessive packet loss, remote access failures to access the hardware, and more; practices that would be unthinkable in public networks. Furthermore, although the preference is for the deployment of cellular networks like 5G, 4G, or 3G, other communication technologies could be supported, like NB-IoT.

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

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

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.

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. 

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 datasets of images and sensor data using ground robots

GRADIANT

Location

At user's premises

Spain

Arable farming

Food processing

Greenhouse

Horticulture

Livestock farming

Tree Crops

Viticulture

The possibility to autonomously gather images and generic sensor data means a quantum leap in the creation of multispectral datasets in an automated and efficient way. A ground robot deployment occurs based on agreed requirements such as study area, measurement granularity, and data type so that it automatically captures all the information needed to generate a rich dataset. This not only makes data collection easy but also reduces human interference, hence reducing the chances of error, thus making the information obtained more valid. After the operation, the data is processed and tidied up to enable further analysis. In that respect, it can deliver faster and more accurate results for the customer, well-treated datasets according to each customer's needs, and at standards that can be further used in artificial intelligence models. With such information, customers will be better informed during the optimisation of their operations by using advanced technologies.

Collection of datasets of images and sensor data using UAV

GRADIANT

Location

At user's premises

Spain

Arable farming

Horticulture

Livestock farming

Tree Crops

Viticulture

The service offers automated drone deployments to efficiently gather images and sensor data, creating comprehensive multi-spectral datasets. The drones operate based on agreed requirements, such as the study area, data type, and measurement granularity, ensuring all necessary information is captured with precision. This makes data collection easy and reduces human interference, hence reducing the risk of error and making the obtained information more valid. After flying, the data is processed and tied up to enable further analyses. The service can deliver fast, comprehensive, and well-treated datasets according to each customer's needs using methods that can be further used in artificial intelligence models. With such information, customers will be better informed during the optimisation of their operations by using advanced technologies.

Collection of test data during physical testing

Politecnico di Milano (POLIMI)

Università degli Studi di Milano (UMIL)

Location

Italy

Arable farming

Food processing

Greenhouse

Horticulture

Livestock farming

Tree Crops

Viticulture

Through this service, we offer dedicated technical systems (hardware and software) and expertise for the collection of relevant data during testing.

The scope of data collected can range from data generated by the AgrifoodTEF technical infrastructure (e.g., through the installation of IoT sensors) to the collection of data generated by the system under test to the generation of ground truth for evaluating the system. If necessary, service S00111 may be leveraged to take care of the interconnection between AgrifoodTEF data collection systems and the customer’s systems.

In addition to the raw collected data, we will also provide customers with complete documentation describing the 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.

Where necessary, data collection and documentation activities can require the involvement of field experts (e.g., engineers, agronomists).

Note: annotation and labelling of collected data, if needed, can be provided via service S00115.