Catalogue of Services

Filtered results

36 results found

Sort by:

Relevance Title

Agronomic dataset generation through optical sensors

We offer a specialised service focused on the acquisition and interpretation of image datasets captured through optical sensors. These datasets provide valuable agronomic indicators such as Leaf Area Index (LAI), biomass, plant count, and more. Key insights that reflect crop health and growth patterns could be generated by analysing these data. These image-based datasets are crucial for training algorithms that predict various agronomic parameters, such as plant growth, height, yield potential, and other important metrics. The measurements of these indicators are carried out on several sites presenting a variety of agro-climatic conditions and allowing the collection of a representative and reliable dataset.

Collection of test data

Data analysis

Provision of datasets

AI model training

Politecnico di Milano (POLIMI)

This service concerns training AI models on behalf of the customer for a specific task and optimisation objective, e.g., improving accuracy on crop classification from image data. The target model is the solution provided by the customer that needs to be enhanced with respect to a set of pre-determined features to reach the desired performance level. However, if required, the training can also be applied to additional state-of-the-art models available in the market for benchmarking purposes. If not defined by the customer, some features of the training process can be identified via service S00179 (desk assessment activities for digital systems and/or data): for instance, model features to improve, reference model baselines to include in the performance comparison, as well as benchmark datasets. The data used for training the model can be either provided by the customer or annotated ad hoc as a preparatory activity to model training (via service S00290 - Data Labelling); another possibility is that data are retrieved among reference benchmark datasets that are openly available. We will also agree with customers on the level of hardware acceleration required, based on the considered AI models: e.g., GPU acceleration via connection to a remote server vs. on-device training.

AI model training

Calibration and Optimisation of Technological Quality Measurement Methods for Cereal Grain

Our service provides access to grain samples and comprehensive technological quality analysis conducted at Arvalis facilities, enabling the development of AI-powered grain analysis solutions. Clients can work with well-documented samples from one or multiple species, enriched with detailed metadata such as variety, harvest year, and collection location crucial for training and validating AI models. Beyond sample selection and preparation, our experts assist in choosing, testing, and validating analytical methods, covering rheological properties (Alvéolab), breadmaking tests, and protein content measurement (Infratec, Dumas). These high-quality datasets, combined with access to Arvalis facilities and controlled testing environments, provide an ideal foundation for developing machine learning algorithms that enhance grain quality prediction, automate classification, and optimise processing parameters. With our service, clients can accelerate the development, validation, and deployment of AI-driven grain analysis tools, ensuring they meet industry standards and deliver precise, reproducible results. Our expertise in analytical methods allows customers to refine their models, improve prediction accuracy, and scale AI solutions for real-world agricultural applications.

Collection of test data

Desk assessment

Performance evaluation

Provision of datasets

Test design

Test execution

Test setup

Collection of test data during physical testing

Politecnico di Milano (POLIMI)

Through this service, we provide specialised technical systems (both hardware and software) and expertise for collecting relevant data during testing.

The scope of the data collected can vary, encompassing information generated by the agrifoodTEF technical infrastructure (e.g., through the installation of IoT sensors), data from the system under test, and ground truth data for system evaluation. If needed, this service can facilitate the interconnection between agrifoodTEF's data collection systems and the customer's systems.

In addition to the raw data collected, we will supply customers with comprehensive documentation detailing the logged features, conditions of the testing environment at the time of testing, and any parameter values, variation ranges, and specifics necessary for reproducibility.

When required, data collection and documentation activities may involve field experts such as engineers and agronomists.

Please note that annotation and labeling of collected data, if necessary, can be provided through service S00115.

Collection of test data

Collection of test data during digital testing

Politecnico di Milano (POLIMI)

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

Data labelling

Politecnico di Milano (POLIMI)

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, the percentage of the full set covered for the annotations, the number of annotators and the inter-annotator agreement.

Data augmentation

Data validation and processing

Politecnico di Milano (POLIMI)

Through this service, we offer customers 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 analysis

Data augmentation

Dataset Generation Based on Agronomic Simulation Models

Our Agronomic Model-Based Dataset Generation service provides agricultural data derived from our internally developed agronomic models, designed and validated by our experts using experimental data. By simulating real-world agricultural conditions, we generate key insights such as growth stage dates for cereals, biomass production, nitrogen requirements, and carbon sequestration, offering highly reliable datasets to support crop modelling, soil analysis, climate impact assessment, and precision agriculture.

Provision of datasets

Design of computational environments for digital testing

Politecnico di Milano (POLIMI)

Test activities involve three key components: the environment (where the tests are conducted), the protocol (defining what tests are performed and how), and evaluation metrics (used to assess the test results). Through this service, we assist customers in designing a computational environment tailored to running the digital tests required for their software components. Specifically, this service:

Defines the data (and metadata) requirements needed to support the test
Identifies the necessary software environment (e.g., operating system, libraries)
Selects an appropriate simulator and defines simulated environments, if required
Chooses remote access tools, if applicable.

As a result, we deliver a comprehensive design of the testing environment, including specifications such as memory requirements, GPU/TPU acceleration, and necessary libraries. This environment can be deployed on either the agrifoodTEF digital infrastructure or the customer's computing infrastructure (i.e., on premises).

Test design

Design of evaluation metrics for digital testing

Politecnico di Milano (POLIMI)

Any test activity involves three main components, i.e., environment (where the tests take place), protocol (defining what activities 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 digital systems such as (for instance) AI models or computer vision software. The digital environment and the testing protocol metrics can be designed—if required—via services S00176 and S00177. Our team will identify and define with customers the most adequate set of quantitative metrics to assess the outcome of the digital testing activities. In order to ensure the relevance of the metrics with respect to the real-world use cases, the team will involve engineers and agronomists. The metrics will be adapted not only to the task that the digital system under test (e.g., a piece of software) is designed to perform, but also to the features of the data used for the tests. For instance, a customer that has developed a machine incorporating an AI model will be interested in testing the model on data generated by their own machine: the performance metrics will therefore need to be adapted to the specific features of that data.

Test design

Design of evaluation metrics for physical testing

Politecnico di Milano (POLIMI)

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).

Test design

Design of Experimental Protocols for Agricultural Applications Using AI Technology or Robotics

Our service offers expertise in the design and implementation of experimental protocols for agricultural applications, ensuring scientific rigor, reliability, and reproducibility. Whether evaluating new agricultural products, testing measurement instruments, or optimising farming practices, we help clients develop tailored protocols that align with industry standards and research best practices. Our experts provide methodological guidance, data collection strategies, and statistical analysis recommendations, enabling clients to generate accurate and meaningful results. By leveraging our expertise, agribusinesses and technology developers can enhance the credibility of their evaluations, accelerate innovation, and optimise decision-making in agricultural research and development.

Collection of test data

People training

Performance evaluation

Test design

Test execution

Test setup

Catalogue of Services

All agrifoodTEF services can be customised!