Catalogue of Services

We offer experimentation fields for arable crops, horticulture crops, and greenhouse facilities. These environments can be complemented with agronomic validation services, sample laboratory analysis or crop characterisation if required.

All combined serve as testbeds for data acquisition throughout the crops’ growth cycle, providing the infrastructure and expertise to support companies in the testing, validation and improvement of their technological solutions in real agricultural conditions.

This includes:

AI-based solutions, such as training and calibration predictive models and algorithms for agronomy applications, including automatic weed detection, biomass and yield estimation, phenological characterisation, or pest and disease monitoring.
Robotic systems and machinery that require physical testing, such as autonomous vehicles, precision spraying equipment, harvesting robots, or treatment platforms.

Provision of datasets

Test setup

Applications of artificial intelligence and visual recognition for assessing the quality of agri-food industry products

The service focuses on utilising artificial intelligence (AI) and visual recognition technology to enhance quality assurance, consistency, and efficiency in the agri-food industry. Its AI-driven quality assessment solutions ensure that products meet industry standards, regulatory requirements, and consumer expectations. By identifying deviations from quality standards, the service enables proactive quality control, reducing the risk of product recalls or rejections.

Certification

Conformity assessment

Data augmentation

National Institute for Research in Digital Science and Technology  (INRIA)

Assessment of AI and robotic solutions for testing

Josephinum Research (JR)

RISE - Research Institutes of Sweden

This service includes all essential actions to gather knowledge, design, and document the client’s needs regarding Testing and Experimentation Facility (TEF) services. Through close collaboration with the client, we ensure that all technical, operational, and methodological aspects are clearly outlined, forming a solid foundation for future testing phases. By conducting these preparatory activities, we deliver a comprehensive setup analysis that serves as the entry point for subsequent testing and validation services and provision of datasets. This ensures that testing can be carried out efficiently, aligning with the client’s specific requirements while leveraging TEF’s expertise and infrastructure. Ultimately, this service streamlines the testing process, reduces uncertainties, and enhances the overall efficiency of AI and robotic solution validation.

Test setup

Blockchain in agriculture traceability systems

The service focuses on testing and developing blockchain technologies to enhance traceability in the agri-food sector. By integrating blockchain with IoT devices, the service ensures a transparent and immutable record of transactions across the entire supply chain. This enables agri-food companies to improve transparency, accountability, and trust while ensuring the integrity and safety of food products. Additionally, it drives efficiency and sustainability throughout the supply chain.

Business modelling

Certification

Conformity assessment

Cybersecurity

Cold chain monitoring

The service focuses on developing and testing devices that ensure proper temperature conditions during the conservation and transportation of agri-food products. These devices capture and store temperature data throughout the process, offering reliable validation of temperature control. Additionally, the service provides advanced data analysis and reporting to help farmers and agri-food operators assess compliance, identify areas for improvement, and meet regulatory requirements.

Business modelling

Certification

Conformity assessment

Collection of test data during physical testing

Through this service, we offer dedicated technical systems (hardware and software) and expertise for the collection of relevant data during testing.

The scope of data collected can range from data generated by the AgrifoodTEF technical infrastructure (e.g., through the installation of IoT sensors) to the collection of data generated by the system under test to the generation of ground truth for evaluating the system. If necessary, service S00111 may be leveraged to take care of the interconnection between AgrifoodTEF data collection systems and the customer’s systems.

In addition to the raw collected data, we will also provide customers with complete documentation describing the logged features and conditions of the testing environment at the time of testing, as well as any parameter values, variation ranges and specifics required for reproducibility purposes.

Where necessary, data collection and documentation activities can require the involvement of field experts (e.g., engineers, agronomists).

Note: annotation and labelling of collected data, if needed, can be provided via service S00115.

Combating forgery in the agri-food chain

The service focuses on developing customised holographic technology to fight the counterfeiting of agricultural products. It provides a tailored security system using advanced holograms that strengthens product integrity, builds consumer trust, and supports the broader goal of safeguarding the agri-food sector from fraud.

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.

Test setup

Data validation and processing

Through this service, we offerers the ability to validate and augment the data collected during the different testing phases. Examples of operations that this service can provide include quality checks (e.g., presence of all datastreams, presence of drop-outs, technical quality such as focus or exposure of camera images, agronomic significance of data); preparation for performance evaluation (e.g., identification of the images containing selected visual markers or specific plants, association between images from the system under test and the ground truth); data processing (e.g., transcoding); and augmentation (e.g., generation and incorporation of metadata). Data labelling (e.g., to enable training of AI models) is not included as it is the object of a separate service (S00291).

Data augmentation

Design of evaluation metrics for physical testing

Any physical test activity involves three main components: environment (where the tests take place), protocol (defining what tests are executed and how) and evaluation metrics (used to assess the results of the tests). This service concerns the last element; its goal is to design the best metrics to evaluate the performance of a customer solution, taking into consideration the use cases specified by the customer and the environment and protocol chosen for the tests (which, if needed, can be designed via services S00106 and S00107).

Our team will identify and define with customers the most adequate set of quantitative (i.e., based on instrumental measurements) and/or qualitative (i.e., relying on expert human judgement) metrics to assess the system functionalities of interest. This phase will involve, in particular, agronomists and experts in agricultural machinery.

Based on the defined evaluation metrics, a set of requirements for the collection of required data and ground truth annotations will also be defined accordingly. For instance, the service may lay out the specifications for dedicated data collection campaigns (possibly executed via service S00113). This phase will involve engineers and experts in AI and robotics.

On request, the output of the service will include analyses on additional environmental factors than those directly tracked through the designed metrics (e.g., seasonal effects, impact of test distribution over time on results).

Design of test environments for physical testing

University of Cordoba (UCO)

Any physical test activity involves three main components: environment (where the tests take place), protocol (defining what tests are executed and how) and evaluation metrics (used to assess the results of the tests). This service concerns the first element, i.e., the design of a physical testing environment for use cases such as (for instance) weeding, plant phenotyping, and precision spraying solutions. The protocol and the evaluation metrics can -if required- be designed via services S00107 and S00108.

Depending on your requirements and reference system/solution to be tested, our team will design an ad hoc setup equipped with all the required features for testing. Environmental features include, for example:

the crop and weed species to be prepared and their growth stage,
the plant layout and intra-row configuration,
seasonal weather and climate-related conditions (e.g., lighting conditions, wind, rain), 
the type of soil, moisture level, and terrain conditions (e.g., uneven terrain, presence of any slopes, and so forth),
the technical infrastructure supporting the tests (e.g., electrical layout, network infrastructure, environmental sensors, data acquisition systems…).

In order to consider all aspects of the environment, this service involves a team comprising both engineers and agronomists.

Evaluation of AI-based optimal pesticide dosage in tree crops

Evaluation of the performance of systems to optimise the dosage of pesticides based on artificial intelligence for tree crops. The service includes comparing the optimised treatment performance versus the standard procedure, measuring the pesticide and economic savings, assessing drift reduction to the environment, and improvements in environmental and biodiversity impact or the CO2 footprint of the treatment.