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User Experience (UX) Testing of AI and Robotic Digital Tools in Agriculture

This service focuses on testing how agricultural professionals interact with AI- and robot-based digital tools such as apps, dashboards or platforms. By observing real or simulated usage scenarios, we identify usability issues and provide practical recommendations. The evaluation is conducted using established UX methodologies, including heuristic evaluation, usability testing and user journey mapping. These techniques help ensure the product is intuitive, efficient, and aligned with the workflows of the agricultural sector. The objective is to support developers and technology providers in enhancing user satisfaction, facilitating adoption, and ensuring the effectiveness of AI and robotic solutions in real agricultural contexts.

Performance evaluation

Test design

Test execution

Test setup

Validation of Integrated Agri Software

This service ensures that the software powering your hardware solution works seamlessly across all components. By testing the integration between different systems—whether it's farm machinery, drones, sensors, or management platforms—we test performance in a practical setting. This helps agribusinesses streamline processes, uncover potential issues, optimise decision-making, and allow comparing different approaches. Whether you're automating irrigation, monitoring crops, or integrating autonomous decision-making software, this service ensures that your technology ecosystem functions cohesively, enabling you to improve efficiency and achieve better outcomes.

Collection of test data

Performance evaluation

Test design

Test execution

Test setup

Validation of Smart Irrigation Technologies

This service offers agrotech companies the opportunity to test and validate the results of smart irrigation technologies in real farming environments. The service includes installing the technology on-site and monitoring its performance under real-world conditions. Metrics such as water usage, crop yield, and system efficiency are analysed to provide a comprehensive validation of the technology's impact. This hands-on evaluation ensures that companies can implement these systems with confidence, knowing their effectiveness in the field.

Collection of test data

Performance evaluation

Test execution

Validation of Yield Estimation based on Computer Vision

Universitat de Lleida (UdL)

Validation of fruit detections, diameter estimation, and yield estimates using artificial intelligence, in particular computer vision techniques.

The service will help the solution provider check its performance using reference datasets or experiments conducted in testing fields. The service might also include the generation of reference datasets and expected detection, diameter measures, or yield values, which can be used both for validation and training of the artificial intelligence solution based on computer vision.

AI model training

Collection of test data

Performance evaluation

Test design

Test execution

Test setup

Valorisation of data and assessment of data space integration

Fondazione Bruno Kessler (FBK)

Analysis of collected data in the context of AI agriculture product or service development and opportunities for data-value extraction via integration within the AgrifoodTEF DataSpace. We can help customers understand the value of their data (monetary and not monetary) and channels to offer it to potential users/buyers. By analysing the offer and demand in relevant Data Spaces, we suggest the optimal way to describe metadata and adopt the right technology implementations to make data visible, ensuring data owners never lose control of who accesses their data. The services include consulting customers on the enrichment of their data system to adopt data sovereignty and trust solutions that make them contribute to and be part of the European data space strategy.

Data analysis

Data augmentation

Vulnerability scan of agrifood-related AI or robotics software systems

Gradiant Technologies

Software systems (like AI and robotics systems developed for agrifood environments) are usually developed using third-party libraries. Regular updates on both proprietary and third-party libraries are strongly recommended to address potential vulnerabilities in the code.

The vulnerability scan for agrifood software systems makes it possible to find out if the developed system has cybersecurity vulnerabilities in its code before its production deployment.
In case vulnerabilities are found, the customer receives information about each one, including the level of criticality and location in the code. Thus, the customer can solve the issues and prevent possible future security problems in the system.

Cybersecurity

Wireless communication infrastructure serving the agricultural sector

Università degli Studi di Napoli Federico II

The service focuses on supporting the development of optimal wireless communication infrastructure for the agricultural sector. By leveraging technologies like Wi-Fi, Bluetooth, cellular networks, and satellite communication, it enables real-time monitoring, data collection, and remote control of agricultural equipment and devices. This infrastructure enhances efficiency, productivity, and sustainability, leading to higher yields, lower costs, and more sustainable farming practices across large agricultural landscapes.

Business modelling

Collection of test data

Data analysis

Performance evaluation

Test execution