Catalogue of Services

This service provides an in-depth evaluation of automation and robotics technologies integrated into machinery for speciality crop operations, including tillage, cover crop management, pesticide application, and harvesting. Through field testing, the service assesses automated systems, robotic vehicles, and their interoperability with other machinery. It is designed to help clients validate the functionality, efficiency, and sustainability of advanced machinery technologies used in precision agriculture for speciality crops.

Politecnico di Milano (POLIMI)

Collection of test data during physical testing

Università degli Studi di Milano (UMIL)

Universitat de Lleida (UdL)

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.

Politecnico di Milano (POLIMI)

Collection of test data during digital testing

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.

Data analysis and quality evaluation for agricultural equipment and AI algorithms

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.

Politecnico di Milano (POLIMI)

Design of test environments for physical testing

Università degli Studi di Milano (UMIL)

Universitat de Lleida (UdL)

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 and testing on the efficiency of SaaS platforms

Evaluation and analysis of the efficiency of algorithms integrated into SaaS platforms that perform specific processes, such as the calculation of parameters from processed data. The primary objective is to detect weak points in the performance of the platform and evaluate the execution times.

People training

Evaluation of crop classification at parcel scale functionality in an agricultural crop management platform

The functionality of crop classification at the parcel level will be assessed using satellite imagery through a platform specialised in agricultural crop management. To evaluate quality, CAP data from prior agricultural seasons will be utilised. This validation process will be applied in Andalusia, Spain, with a focus on the region's agroclimatic conditions and crop phenology.

Evaluation of customer UAV-collected test data from physical testing

This service applies pre-defined performance metrics to UAV-collected test data from customers and corresponding ground truth data to assess the effectiveness and reliability of tested systems. The service includes a detailed evaluation and reporting process, offering clients valuable insights into system performance and supporting their technological validation needs. This evaluation is crucial for clients looking to understand the accuracy and robustness of their UAV-based data collection tools in agriculture.

Evaluation of monitoring technologies for parameters in harvesters and equipment used in the pre- and post-harvest phases of speciality crops

This service provides physical testing and evaluation of parameter monitoring technologies integrated into harvesters and post-harvest equipment for speciality crops, particularly in olive farming. Field assessments cover a range of technologies, including yield monitors, harvest mapping, fleet management systems, remote diagnostics, and fruit evaluation tools. By focusing on the pre- and post-harvest phases, this service ensures that sensor performance and measurement accuracy meet industry standards, supporting sustainable and precise agricultural practices.

Evaluation of onboard technologies in tractors and machinery

Location

Spain

Tree Crops

This service provides comprehensive field testing for onboard technologies in tractors and machinery. The evaluation includes the functionality of sensors and measurement systems integrated into the machinery, covering production mapping, fleet management, remote diagnostics, traceability, and the use of machinery simulators. The service supports clients in validating the effectiveness of their equipment for sustainable operations and improving technology-driven management.

Evaluation of sprayer boom for spot spraying site-specific applications

This service evaluates the efficiency of sprayer booms equipped with sensors that use artificial vision to detect weeds and enable site-specific phytosanitary applications. Through field tests and drone-assisted monitoring, the service examines how accurately the sprayer boom applies treatments, identifying false positives (detecting weeds where none exist) and false negatives (failing to detect actual weeds). The service is essential for improving precision agriculture practices in arable and horticultural crops, as well as in tree crops and viticulture.

Evaluation of sprayers for tree crops for site-specific applications

Location

Spain

Tree Crops

This service assesses the efficiency of sprayer systems designed for tree crops, focusing on site-specific applications of phytosanitary treatments. By integrating sensors such as 3D cameras and canopy maps, the service evaluates canopy volume detection and distribution accuracy. Field testing includes drone flights and hardware implementation to monitor sprayer performance. This service is ideal for precision agriculture applications in tree crops, providing insights into equipment effectiveness for sustainable and targeted treatment distribution.

Market research