Catalogue of Services

Data analysis

Provision of datasets

Test execution

AI model training for computer vision tasks in TEF infrastructure

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.

AI model training

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

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 and LoRaWAN, other communication technologies could be supported, like NB-IoT.

Test execution

Assessment of the feasibility of integrating payloads or equipment into UAVs

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.

Assessment of the feasibility of integrating payloads or equipment into UGVs

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.

Collection of datasets of images and sensor data using ground robots

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.

Provision of datasets

Collection of datasets of images and sensor data using UAV

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.

Universitat de Lleida (UdL)

Provision of datasets

Computer vision for automated food quality assessments

Validation of an existing automated food quality assessment solution based on Artificial Intelligence, in particular Computer Vision techniques. The service will help the solution provider check its performance using a controlled testing infrastructure, including our Food Processing Pilot Plant. The service might also include the generation of reference datasets of food samples and expected quality measures, which can be used both for validation and training of the Artificial Intelligence solution based on Computer Vision.

AI model training

Test execution

Container image generator that facilitates efficient software testing and deployment

Container images have become the de facto standard for software delivery in a variety of industries, including agrifood. Packaging an application and all its dependencies in a container image ensures consistency across different deployment environments, allowing these applications to be tested more quickly and efficiently. Once the container image is built, it can be run by different container management tools, for example, Docker or Podman.

However, creating secure and optimised container images is often complex. This service simplifies the process by automatically generating container images from the application source code. It detects dependencies and environmental requirements, removing the need for the customer to perform and/or to possess technical expertise in container technologies.
The process is fully automated: the user provides the source code reference with secure access, and the service handles the rest. This eliminates the need to manage image creation files and reduces potential human error during the image creation phase, allowing developers to focus on innovation and functionality.

University of Cordoba (UCO)

Data analysis and quality evaluation for agricultural equipment and AI algorithms

This service provides independent evaluation of agricultural datasets to determine their suitability for use in the testing, development, or validation of AI- and robotics-based systems. The focus is on verifying the quality, structure, and statistical consistency of the data to ensure it meets the requirements for use in intelligent technologies operating in agricultural environments. Our evaluation process includes the application of descriptive statistical techniques to assess data completeness, identify anomalies, and quantify variability across key parameters such as crop yields, irrigation records, and fertilisation schedules. We assess measures of central tendency, dispersion, and distribution to evaluate the stability and reliability of the datasets. This helps identify issues like missing values, outliers, or inconsistencies that could compromise the performance or fairness of automated systems trained or tested on this data. By systematically evaluating data integrity and structure, we help researchers, developers, and integrators ensure their AI algorithms or robotic platforms are tested with datasets that reflect real-world conditions. This contributes to more effective experimentation, better system generalisation, and ultimately, more trustworthy agricultural technologies.

Data Analysis for Smart Irrigation Systems

This service allows companies to experiment with real-time data and advanced analytics to improve water management, reduce waste, and enhance crop yield. The service includes comprehensive testing and evaluation to ensure compatibility and performance, ensuring efficient irrigation practices and sustainability through insights into soil moisture, weather conditions, and plant water needs.

Data analysis

Dataspace use case design analysis

Creating a dataspace involves significant costs and time investment. Technology-related expenses include evolving and adapting reference software components (such as participant agents (connectors), identity and access management mechanisms, and data catalogues), designing the dataspace, and deploying it in a public or private cloud infrastructure. To justify these costs, a compelling value proposition for each data space participant (consumers or providers) is crucial.
The primary aim of the service is to assist the customer in designing well-defined dataspace use cases. The service offers aid in the definition of the purpose, review of the participant roles (that could be data provider, data consumer, data intermediary, or part of the data space governance authority), business and governance models outline, and specification of the reference architecture and user interface of the dataspace.
Tools used in this process include the Starter Kit for Data Space Designers [1], Data Cooperation Canvas [2], Use Case Playbook [3], and Use Case Blueprint [4].

[1] https://dssc.eu/download/attachments/29523973/DSSC-Starterkit-Version-1.0.pdf?download=true
[2] https://www.datacooperationcanvas.eu/canvas/intro
[3] https://internationaldataspaces.org/wp-content/uploads/dlm_uploads/use-case-playbook.pdf
[4] https://dataspacessupportcentre.atlassian.net/wiki/spaces/BVE/pages/357074241/Use+Case+Development