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Assessment of the feasibility of integrating payloads or equipment into UAVs

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.

Desk assessment

Assessment of the feasibility of integrating payloads or equipment into UGVs

This service will evaluate the feasibility of securely integrating third-party equipment or payloads related to the agrifood sector into unmanned ground vehicles (UGVs). 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 UGV. 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.

Desk assessment

Assistance for Identifying AI Technologies for Agricultural Robotics, Drones, and Precision Machinery

Danish Technological Institute (DTI)

Our service is tailored to support AI technology providers, particularly in robotics and drone sectors, focusing on agricultural applications. We identify AI technologies that boost agricultural efficiency and productivity and offer integration support for embedding AI into agricultural robotics, drones, and precision machinery. Our guidance includes selecting effective AI tools and algorithms for specific agricultural challenges. Additionally, we offer comprehensive screening of AI technologies for agricultural relevance, which supports the company’s seamless integration of AI into existing equipment.

AI model training

Desk assessment

People training

Collection of test data during digital testing

Politecnico di Milano (POLIMI)

Università degli Studi di Milano (UMIL)

One of the key activities during digital testing is the collection of data concerning the progression and the final outcome of the tests. Such data enable the evaluation of system performance by the customer or – if needed – by agrifoodTEF (via Service S00184). This service manages the collection of data relevant to performance evaluation produced during the tests by both the system under test and the computational environment where the tests take place.

Examples of collected data comprise information produced within a virtual environment to simulate sensor data collection in a physical environment; statistics about AI model performance in the test and deployment phase (e.g., occupied memory, number of trainable parameters, training/optimisation loss, etc.); specific labels and annotations to use as ground truth for evaluating the system; and system output when subjected to a range of test conditions.

The minimum set of data to be collected is defined by the evaluation metrics that the user chose (either on their own or with agrifoodTEF support, via Service S00178) to process them; generally, a larger set of data wrt the minimum is selected by agrifoodTEF together with the customer to provide a richer view of the system’s performance and to enable the application of other metrics in the future, if needed.

As an output of the service, in addition to the raw data, we also provide the customer with documentation describing 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.

Collection of test data

Container image generator that facilitates efficient software testing and deployment

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.

Test setup

Crop Dataset Generation

Instituut voor Landbouw-, Visserij- en Voedingsonderzoek (ILVO)

In the Crop Dataset Generation service, we collect large-scale datasets on a frequent basis using drones or autonomous robots. AI and data-driven solutions require extensive data, shifting the focus from limited feature engineering to acquiring large, balanced datasets of images and labels for training convolutional deep-learning networks. We offer flexible data collection options tailored to customer needs. We can use our systems and fields to gather data on crops under various conditions, such as different lighting, angles, growth stages, and soil types. Alternatively, we can use customer-provided devices to collect data for testing and validation, such as evaluating sensor performance in detecting heat in crops or testing robot navigation in crop fields.

Collection of test data

Provision of datasets

Curation and provision of annotated datasets

Lukasiewicz Poznanski Instytut Technologiczny (L-PIT)

Poznan Supercomputing and Networking Center (PSNC)

The service provides access to annotated data collected from devices tested as part of other project services. It includes both data annotation services (manual and automatic) and the provision of annotated databases or data packages. In addition, we offer disc space for temporary archiving of the collected data along with its annotation in cases where this is not possible at the client's site. As a result of the service, procedures for both manual and automatic annotation will be identified that are appropriate to the type of measurement data and the way it is collected and assembled. The methods of labelling and assigning specific data classes collected by the agriFoodTEF technical infrastructure will be proposed. Possibilities for documenting and storing data in datasets will be indicated. In addition, procedures for extracting selected features from stored research data can be offered.

Collection of test data

Provision of datasets

Data analysis and quality evaluation for agricultural equipment and AI algorithms

University of Cordoba (UCO)

This service provides independent evaluation of agricultural datasets to determine their suitability for use in the testing, development, or validation of AI- and robotics-based systems. The focus is on verifying the quality, structure, and statistical consistency of the data to ensure it meets the requirements for use in intelligent technologies operating in agricultural environments. Our evaluation process includes the application of descriptive statistical techniques to assess data completeness, identify anomalies, and quantify variability across key parameters such as crop yields, irrigation records, and fertilisation schedules. We assess measures of central tendency, dispersion, and distribution to evaluate the stability and reliability of the datasets. This helps identify issues like missing values, outliers, or inconsistencies that could compromise the performance or fairness of automated systems trained or tested on this data. By systematically evaluating data integrity and structure, we help researchers, developers, and integrators ensure their AI algorithms or robotic platforms are tested with datasets that reflect real-world conditions. This contributes to more effective experimentation, better system generalisation, and ultimately, more trustworthy agricultural technologies.

Collection of test data

Performance evaluation

Test design

Data analysis to optimise AI model training

Josephinum Research (JR)

Our service enhances your dataset's quality using descriptive statistics to gain insights and ensure robustness. We use measures like mean, median, standard deviation, etc., to identify central tendencies and variability. We perform plausibility filtering to check for logical consistency and outlier filtering to remove data points that could skew predictions. This careful data preparation refines your dataset, boosting the accuracy and reliability of your model training. Tailored to your needs, these methods optimise your dataset for better model performance.

Data analysis

Data augmentation to optimise AI model training and prevent the model from overfitting

Josephinum Research (JR)

In preparing image or sensor data, pre-processing is crucial. We apply techniques like normalisation for data uniformity and correlation analysis to uncover patterns, enhancing interpretability. Dimension reduction optimises computational efficiency, while adding randomised noise and artificial expansion improves model generalisation. We implement anonymisation to protect data privacy and ensure precise data labelling for supervised learning. These methods, tailored to your needs, enhance your data's quality and usability.

Data augmentation

Data labelling

Politecnico di Milano (POLIMI)

Università degli Studi di Milano (UMIL)

This service concerns the curation of ground truth annotations for testing and experimentation data. More specifically, data labelling concerns the association of portions of data (e.g., image regions) to descriptions that provide information about the significance of those portions. In particular, labelling is required whenever the data are meant to be used for model training.

We also provide labelling services for domain-specific data attributes, such as disease indicators and their confounding factors, by experts in agronomy. The labelled data will be accompanied by a report synthesising quality metrics and statistics like the number of data points annotated, percentage of the full set covered for the annotations, number of annotators and the inter-annotator agreement.

Data augmentation

Data pipeline evaluation for AI applications

RISE - Research Institutes of Sweden

The purpose of the service is to ensure that data protocols and pipelines are adequate to the AI application developed by the customer. The service can be delivered at different stages of product maturity to ensure that it can provide targeted testing when it is most useful. During earlier product development stages, the service concentrates on data collection, quality and storage protocols, whilst, at later stages, attention can be extended to scalability, access and control mechanisms.

AI model training

Collection of test data

Data analysis

Performance evaluation

Test execution

Test setup

Catalogue of Services

Our infrastructures serve various sectors and countries!

Which agrifood sector are you interested in?

Assessment of the feasibility of integrating payloads or equipment into UAVs

Assessment of the feasibility of integrating payloads or equipment into UGVs

Assistance for Identifying AI Technologies for Agricultural Robotics, Drones, and Precision Machinery

Collection of test data during digital testing

Container image generator that facilitates efficient software testing and deployment

Crop Dataset Generation

Curation and provision of annotated datasets

Data analysis and quality evaluation for agricultural equipment and AI algorithms

Data analysis to optimise AI model training

Data augmentation to optimise AI model training and prevent the model from overfitting

Data labelling

Data pipeline evaluation for AI applications

Interested in exploring the services from the agrifoodTEF Catalogue?

Catalogue of Services

Our infrastructures serve various sectors and countries!

Which agrifood sector are you interested in?

Assessment of the feasibility of integrating payloads or equipment into UAVs

Assessment of the feasibility of integrating payloads or equipment into UGVs

Assistance for Identifying AI Technologies for Agricultural Robotics, Drones, and Precision Machinery

Collection of test data during digital testing

Container image generator that facilitates efficient software testing and deployment

Crop Dataset Generation

Curation and provision of annotated datasets

Data analysis and quality evaluation for agricultural equipment and AI algorithms

Data analysis to optimise AI model training

Data augmentation to optimise AI model training and prevent the model from overfitting

Data labelling

Data pipeline evaluation for AI applications

Interested in exploring the services from the agrifoodTEF Catalogue?

Interested in exploring the services from the agrifoodTEF Catalogue?