Services

AI Ethics

RISE - Research Institutes of Sweden

Location

Remote

Sweden

Aquaculture

Arable farming

Forestry

Food processing

Greenhouse

Horticulture

Livestock farming

Tree Crops

Viticulture

The AI Governance Framework Evaluation service empowers agricultural businesses to leverage AI responsibly. It addresses the unique challenges and opportunities within the sector, from precision farming and livestock management to supply chain optimisation. This service starts with a use-case assessment that highlights similar AI initiatives and identifies potential ethical gaps. Following that, a tailored AI ethics framework is developed, aligned with organisational values and industry best practices, encompassing data privacy, algorithmic fairness, and transparency. A comprehensive impact assessment of AI systems is conducted, identifying potential biases and risks related to environmental impact, labour practices, and food security. Mitigation strategies, including technical solutions and governance processes, are developed to address these risks. Ongoing support and monitoring ensure AI remains ethically sound and compliant with regulations such as the GDPR, the AI Act, the Machinery Regulation, the Data Governance Act (and the Open Data Directive), and others, fostering trust with farmers, consumers, and the broader community. The service does not provide legal advice, but it evaluates the organisation’s ethics and legal readiness and provides recommendations for further steps when needed.

AI Ethics training

RISE - Research Institutes of Sweden

Location

At user's premises

Remote

Sweden

Aquaculture

Arable farming

Forestry

Food processing

Greenhouse

Horticulture

Livestock farming

Tree Crops

Viticulture

The AI Ethics Training programme trains engineers working with AI to detect, analyse, and address ethics issues during the design and development phases. As a result, it provides organisations within the agricultural sector the essential knowledge and practical skills required for responsible AI implementation. This service addresses the critical need for teams to understand and learn how to apply ethical principles in their daily work with AI systems. The training covers key areas, including data privacy, algorithmic fairness, and transparency, ensuring alignment with industry best practices and evolving regulatory standards. Participants gain proficiency in identifying and mitigating potential ethical risks associated with AI deployment. Through interactive workshops and real-world case studies tailored to agricultural applications, organisations can foster a culture of ethical AI development and maintain stakeholder trust. Investing in this training ensures the responsible and sustainable integration of AI technologies, strengthening the organisation's position as a leader in ethical innovation within the agricultural industry.

AI Experiment

RISE - Research Institutes of Sweden

Location

At user's premises

Remote

Sweden

Aquaculture

Arable farming

Forestry

Food processing

Greenhouse

Horticulture

Livestock farming

Tree Crops

Viticulture

The AI Explainability Audit service offers independent assessment of AI explainability in agriculture, focusing on the results of AI systems, not the technical details. We examine the explanations provided by AI systems (e.g., for crop management, livestock care, or resource optimisation) to ensure they are clear, accurate, and relevant to real-world farming practices. The service assesses whether these explanations are easily understood by farmers and other stakeholders, identifying any gaps or potential biases in how the AI communicates its reasoning. The service focuses on evaluating the output of AI systems. We analyse the explanations provided to users, using methods like document review and expert analysis (without needing access to the raw data or code itself). This helps agricultural organisations build trust in their AI tools, ensure ethical and sustainable practices, and meet emerging regulatory requirements related to AI transparency in agriculture, all without requiring technical skills from their side.

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

Aquaculture

Forestry

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.

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

GRADIANT

Location

At user's premises

Spain

Arable farming

Food processing

Greenhouse

Horticulture

Livestock farming

Tree Crops

Viticulture

Aquaculture

Forestry

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.

Assessment of data architecture for agrifood applications

GRADIANT

Location

Remote

Arable farming

Food processing

Greenhouse

Horticulture

Livestock farming

Tree Crops

Viticulture

Aquaculture

Forestry

This service helps agrotech companies optimise and scale their existing software and AI solutions. We evaluate your current technology stack, data architecture, and system performance to identify opportunities for improvement and growth. Our expert team analyses key aspects such as system resilience, scalability potential, and integration capabilities to provide actionable recommendations. Through a systematic testing review process, we help you understand how your solution can handle increased workloads, adapt to new use cases, and maintain performance as your business grows. We assess critical factors like data management, processing efficiency, and system architecture, translating technical challenges into clear business opportunities. Our recommendations focus on practical steps to enhance your solution's reliability, expand its capabilities, and prepare it for future growth. The service includes detailed analysis reports, specific improvement recommendations, and strategic guidance for implementing changes, ensuring your agrifood technology solution is ready to scale and meet evolving market demands.

Assessment of the feasibility of integrating payloads or equipment into UAVs

GRADIANT

Location

Remote

Spain

Arable farming

Horticulture

Livestock farming

Tree Crops

Viticulture

Aquaculture

Forestry

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.

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

Aquaculture

Forestry

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.

Container image generator that facilitates efficient software testing and deployment

GRADIANT

Location

Remote

Spain

Arable farming

Food processing

Greenhouse

Horticulture

Livestock farming

Tree Crops

Viticulture

Aquaculture

Forestry

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.

Data quality analysis of data to be used in agrifood AI or robotics systems

GRADIANT

Location

Remote

Arable farming

Food processing

Greenhouse

Horticulture

Livestock farming

Tree Crops

Viticulture

Aquaculture

Forestry

Given a dataset intended to be used in AI or robotics applications for the agrifood sector, a quality analysis will be performed to search for issues in the provided data. Thus, the customer can ensure their data is reliable and suitable for use in this kind of application. The dataset could be an input or an output of an AI process. For example, it could be data to train a predictive system or the output of that predictive system (which allows detecting prediction problems, like predictions out of range or not following the expected output distribution). The kind of issues to search for range from missing values to problems related to data out of distribution. As a result of this service, the client will receive a report with the conclusions and recommendations on how to improve the data, if needed.

Dataspace use case design analysis

GRADIANT

Location

Remote

Spain

Arable farming

Food processing

Greenhouse

Horticulture

Livestock farming

Tree Crops

Viticulture

Aquaculture

Forestry

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 

Deployment of an AI model for agrifood application testing

GRADIANT

Location

Remote

Arable farming

Food processing

Greenhouse

Horticulture

Livestock farming

Tree Crops

Viticulture

Aquaculture

Forestry

Our service provides a controlled testing environment to evaluate how AI models interact with your agricultural or food processing applications. We set up the necessary infrastructure to host and run the AI model you want to test, making it accessible through a standardised interface during the testing period. Instead of permanently integrating an AI model into your system, this service lets you first validate its performance and behaviour in a controlled setting. We configure our infrastructure to host the AI model and create a testing setup where your application can interact with it remotely. This allows you to thoroughly assess how the AI model performs as part of your system before making any commitments to full integration. The service includes setting up the testing environment, configuring the necessary interfaces for your application to communicate with the AI model during testing, and providing the technical documentation needed for the testing phase. This approach helps you evaluate AI capabilities in a low-risk environment while maintaining your existing systems' integrity.