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Testing of physical systems with embedded AI or robots

Institut de l‘Elevage - IDELE

This service is designed to facilitate the testing and evaluation of cutting-edge technologies with embedded AI and robotics. This service provides companies with access to state-of-the-art infrastructure tailored for testing physical systems in real-world agricultural environments. From autonomous robots to AI-driven breeding tools, the service offers both technical support and comprehensive testing facilities, enabling the validation of these innovations under practical conditions. This ensures that new technologies meet the stringent demands of modern farming practices. Our team will provide expert guidance and support throughout the testing process, helping customers to make the most use of our facilities, ensuring security, and adequate exploitation.

Collection of test data

Desk assessment

Test execution

Testing of plant detection

Our digital test helps you evaluate how well your model identifies different types of plants. Your model can be delivered in a docker container or the inference results are collected in real time from your device in the field. We focus on key performance metrics like accuracy, precision, recall, Intersection over Union (IoU), and F1 score to give you a clear picture of your model's effectiveness. Crucial to this process is the ground truth data, which can be either specially prepared for this test or sourced from an existing benchmark dataset.

Testing of plant health assessment tools for high-value crops

Lukasiewicz - Poznanski Instytut Technologiczny

The service is intended to provide the client with technological and intellectual support for the improvement of plant health assessment tools for high-value crops. Support will be provided through testing of optical technology solutions, image analysis pipelines, and AI systems. The proposed testing and analysis will provide a wealth of feedback on the relevant tools and the quality and utility of the crop health data they collect. It may lead to changes in components, tools, and the choice of software and algorithms and general suggestions to ensure safe and effective work with sensitive crops.

Testing of spot spraying performance

Our spot spraying test service precisely checks how well your spraying system works under real-world conditions. The innovative approach allows for immediate documentation of the spray distribution without altering the spraying process. After application, we document the spread pattern and analyse this data to assess how effectively and precisely the liquid targets the intended areas. This enables you to fine-tune application techniques and evaluate herbicide effects, ensuring your spraying strategies are both efficient and environmentally conscious.

Collection of test data

Performance evaluation

Test design

Test execution

Test setup

Testing of the follow-me function of a robot

Our service offers a comprehensive evaluation of your device's ability to detect and track moving objects like people, ensuring it performs reliably in real-world scenarios. Also, safety concerns of such a function can be addressed with our tests. Through field tests, we simulate realistic conditions to test your system under various environmental influences. This practical approach provides precise insights into your device's effectiveness and highlights areas for improvement, making it essential for startups and SMEs in the tech-agricultural sector aiming to refine their innovative solutions.

Collection of test data

Performance evaluation

Test design

Test execution

Test setup

Testing of the person detection function of a robot

This service tests the robustness and accuracy of a solution to detect moving objects, such as people. We conduct these tests manually in the field to ensure that your device is evaluated under realistic conditions, providing you with reliable insights into its performance and areas for improvement. The test will be done under various conditions (different illuminations, rain, sunshine, diffuse light, etc.) to address the needs of your customers. You will receive a report of the system's capabilities as well as under which conditions the detection should be improved.

Collection of test data

Performance evaluation

Test design

Test execution

Test setup

Testing of translation solutions for multilingual applications

Fondazione Bruno Kessler

Agriculture is a local and specific business, where localisation of solutions is required, especially when it comes to language and interfacing with users. Albeit the diffusion of multilingual generative AI technologies (or large language models, e.g., GPT), these models are not optimised for a specific industry. We can test the performance of multilingual AI solutions in agriculture, highlighting bottlenecks that limit them. This allows for optimised localisation of solutions and quicker achievement of scalability at the European level.

Performance evaluation

Test design

Test execution

Test setup

Testing of weeding performance

Our service offers comprehensive field trials to test the performance of your AI-based weeding system. Before the system operates, we record essential environmental data such as soil conditions and weather. We assess the crops and weeds either manually or with automated tools, and re-evaluate them post-weeding to measure the system's effectiveness. Our trials can be conducted on fields provided by us or at external locations of your choosing. This testing gives you clear, objective insights into your system’s performance and helps identify any potential issues under real-world conditions.

Collection of test data

Performance evaluation

Test design

Test execution

Test setup

Testing or verification of the soil sample size needed for irrigation model recalibration

University of Cordoba

This service involves the testing or verification of the soil sample size needed for the recalibration of the model in intelligent irrigation systems. It includes physicochemical parameters of the soil, and variable distribution of water and supplies. Observations are systematic and cover different conditions and crops to verify model accuracy and the effectiveness of site-specific input distribution.

Collection of test data

Data analysis

Performance evaluation

Test design

Test execution

Test setup

Testing the integration and performance of advanced weather forecasting

Fondazione Bruno Kessler

Solutions in the agricultural sector that use weather forecasts can suffer from inaccuracies of standard weather service APIs, such as lack of granularity, no usage of in-field sensors, and low precision of generic models. Using FBK's expertise in advanced weather forecasting, we are capable of testing weather-related functionalities in solutions and proposing ways forward to take it to the next level: nowcasting, higher resolution, model optimisation for specific regions, etc.

AI model training

Performance evaluation

Test design

Test execution

Test setup

Testing the recalibration of irrigation model through soil moisture sample values, soil sensors, and crop management data

University of Cordoba

Irrigation models are validated using simulations based on various data sources, such as climatic and crop data. This ensures accurate water distribution, reduces waste, and enhances crop yields across diverse conditions. It includes physicochemical parameters of the soil, and variable distribution of water and supplies. Observations are systematic and cover different conditions and crops to verify model accuracy and the effectiveness of site-specific input distribution.

Collection of test data

Data analysis

Performance evaluation

Test design

Test execution

Test setup

Tracking of AI development lifecycle

This service helps you keep track of everything that happens while training and testing AI systems. Think of it like a detailed diary that automatically records all the important information about your AI models. Every time you make changes to your model, train it with new data, or test its performance, our system saves this information in an organised way. You get access to a user-friendly website where you can see all your models, compare how well different versions are performing, and understand what changes led to better results. For example, if you're training an AI model to count fruits on trees, you can easily compare how accurate different versions of your model are, see which training data worked best, and track how each change improved the counting accuracy. The system stores all the technical details, test results, and performance measurements in one place, making it simple to find information when you need it and share results with your team. This takes away the headache of manually documenting everything and helps you make better decisions about which version of your AI model to use.

AI model training

Catalogue of Services

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