Catalogue of Services

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Deployment of an AI model for agrifood application testing

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.

Test setup

Design of AI algorithm test scenario

This service helps organisations evaluate and validate their AI solutions and datasets in a systematic way. We work with you to design comprehensive test scenarios that match your specific needs and objectives. Whether you want to test your existing AI model's performance, validate a dataset's quality, or compare different machine learning approaches, we follow standard ML development practices to create a structured testing process. This includes designing the testing environment, defining test protocols, establishing evaluation metrics, and setting key parameters. Using TEF's infrastructure, we implement these elements to create test conditions that reflect real-world usage. We can work with both your own AI solutions and datasets or help you select appropriate ones from TEF's resources. This systematic approach ensures you get clear, actionable insights about your AI solution's performance, reliability, and potential areas for improvement. The testing scenarios are designed to be transparent, repeatable, and aligned with industry best practices.

Test design

Evaluation of AI model for agrifood applications

This service helps businesses and organisations evaluate how well their AI models perform in real-world conditions. We conduct thorough testing of your AI model using either your own dataset or our reference datasets, depending on your needs (see the related services). Our evaluation process goes beyond basic accuracy metrics to provide a comprehensive understanding of your model's strengths and limitations. We analyse various aspects of performance that matter for your specific use case, whether it's detecting diseases in crops, sorting produce, or monitoring livestock behaviour. We work with you to determine the most relevant performance metrics for your application, taking into consideration factors like accuracy, speed, reliability, and resource usage. This helps you understand if your AI solution is ready for deployment, needs improvement, or requires adjustments for specific conditions. The evaluation provides clear, actionable insights about your model's performance, helping you make informed decisions about its readiness for real-world agricultural applications.

Performance evaluation

Execution of AI testing

The execution AI testing service takes your existing test definitions and handles their execution within our specialized testing infrastructure. We manage the intricate setup process required for each test scenario, ensuring all parameters and conditions are precisely configured to match your requirements. Furthermore, we also offer a test scenario design service that can be executed prior to this service (see related services). Our expert team oversees the complete testing cycle, from initial environment preparation through result collection. After test completion, you receive a detailed report documenting all test outcomes, observations, and relevant measurements. This service is particularly valuable for sophisticated testing needs that demand careful setup and expert knowledge. We understand that certain test scenarios require nuanced configuration and specialised expertise. Our service bridges this gap by providing both the necessary infrastructure and professional oversight to ensure your tests are executed accurately and yield reliable results. The comprehensive reports we deliver enable you to make informed decisions based on thorough test data.

Test execution

Test setup

Generation of datasets using network-connected sensors

This service focuses on the collection and processing of data from sensors connected to a 5G network. Leveraging the high-speed, low-latency capabilities of 5G, it facilitates efficient data transmission, ensuring comprehensive coverage and timely data collection across various environments. The collection and processing methods yield high-quality datasets that are essential for training AI models and developing decision-making algorithms, supporting diverse applications. Additionally, the scalable environment developed allows for adaptability across different sensors, use cases, locations, and even communication technologies, like LoRa.

Provision of datasets

Generation of quality terrain datasets over autonomous ground robots

This service specialises in generating high-quality terrain datasets using sensor-equipped UGVs (unmanned ground vehicles). By employing autonomous UGVs, the service streamlines the data collection process, ensuring efficiency and accuracy in gathering crucial parameters like humidity, pH, temperature, etc. This equipped UGV navigates diverse terrains, autonomously collecting data in specific locations planned by the user. The automated data collection not only enhances the speed of information gathering but also facilitates the generation of comprehensive datasets. This information is invaluable for professionals seeking to make AI models, make data-driven decisions, and analyse the impact of different strategies on the lands.

Collection of test data

Provision of datasets

Life Cycle Assessment (LCA) for Agriculture Robots

Danish Technological Institute (DTI)

Life Cycle Assessment (LCA) for Agriculture Robots is a comprehensive evaluation method that assesses environmental impacts at every stage of a robot’s lifecycle—starting from raw material extraction, manufacturing, utilization, and ending with disposal or recycling. This service provides valuable insights into energy consumption, waste generation, and other environmental factors, helping customers identify key areas for improvement. By promoting sustainability and eco-efficiency, LCA supports decision-making for reducing ecological footprints and ensuring compliance with sustainability standards such as Frugal AI requirements. The service is tailored to meet diverse customer needs, enabling them to understand and optimize the environmental performance of their robotic technologies.

LCA assessment

Model training of AI agrifood-related algorithms

The AI model training service in TEF infrastructure offers a powerful platform for developing cutting-edge artificial intelligence solutions in the agrifood sector. Leveraging high-performance computing resources, this service enables innovators to train AI models using diverse tabular data types. These include sensor readings (such as soil moisture, temperature, and nutrient levels), machine-generated data (from agricultural equipment and IoT devices), crop yield statistics, weather patterns, and supply chain metrics. Users can either provide their own datasets or utilise existing data within the TEF infrastructure (see Related Services). The service supports a wide range of AI frameworks and model architectures, allowing for flexible experimentation and rapid iteration. From predictive maintenance of farming equipment to optimising crop management practices, this platform accelerates the development of AI-driven solutions that address critical challenges in agriculture and food science, fostering innovation and efficiency in the industry.

AI model training

Monitoring of deployed AI models for agrifood applications

Our monitoring service provides comprehensive visibility into your own current model deployments. Through an intuitive dashboard interface, stakeholders can track and analyse their model's performance in real-time. The service continuously monitors three critical aspects: computational resource utilisation (such as processing power and memory usage), incoming data streams and patterns, and the model's output results and accuracy. This visibility allows teams to proactively identify potential bottlenecks, optimise resource allocation, and ensure the model performs as expected. The dashboard presents complex technical metrics in an easy-to-understand visual format, enabling both technical and non-technical team members to make informed decisions about their deployment. By providing these monitoring capabilities, the service helps maintain optimal performance and reliability of your model within the test environment while reducing the time needed to identify and resolve potential issues. The deployment of an AI model for testing purposes could also be done by Gradiant on another service (see related services) and then be monitored through this service.

Performance evaluation

Real-World AI Model Testing for Agricultural Applications

Danish Technological Institute (DTI)

This service involves deploying AI models in authentic agricultural environments to validate their predictions against real-world data. The process includes field deployment where AI models are integrated with drones or other data collection systems. Ground truth data is collected using manual measurements or industry-standard equipment, serving as a benchmark for accuracy evaluation. Models are compared against this data to assess performance and robustness across various conditions. If required, a feedback loop can be established to refine models based on discrepancies found, ensuring continuous improvement and reliable predictions for agricultural decision-making.

Collection of test data

Data analysis

Desk assessment

Performance evaluation

Test design

Test execution

Test setup

Safety and Regulatory Testing for Agricultural Robotics

Danish Technological Institute (DTI)

This service offers a thorough examination of agricultural robots, focusing on safety validation under diverse operational conditions. It tests stopping distances and response times for mobile robots across various speeds and payloads. Regulatory compliance is assessed to ensure adherence to industry standards. The service utilizes a controlled testing environment that simulates agricultural field conditions, equipped with measurement tools to analyze stopping distances and robot dynamics. Parameters can be adjusted to test different speeds, payloads, and environmental factors, minimizing risks and enhancing reliability in dynamic farm environments.

Collection of test data

Conformity assessment

Testing AI Solutions for Precision Agriculture

Danish Technological Institute (DTI)

This service focuses on testing and experimenting with AI solutions specifically designed for agricultural applications. It aids companies in robotics and drones by facilitating the implementation and deployment of advanced technologies in farming and food processing environments. The service includes the evaluation of new sensors and algorithms that enhance localisation and positioning, crucial for precise agricultural operations. We offer testing and validation of AI-driven solutions for precision agriculture, support for implementing robotics and AI in agricultural practices, optimisation of sensor and algorithm performance for enhanced crop monitoring and yield prediction, and deployment of drones for aerial surveillance and data collection on large farms.

AI model training

Collection of test data

Data analysis

Performance evaluation

Test design

Test execution

Test setup