Catalogue of Services

AI model training

Politecnico di Milano (POLIMI)

Location

Italy

Remote

Arable farming

Food processing

Greenhouse

Horticulture

Livestock farming

Tree Crops

Viticulture

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.

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

Università degli Studi di Napoli Federico II

Location

Italy

Arable farming

Food processing

Greenhouse

Horticulture

Livestock farming

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.

Assessment of AI performance and software optimisation on edge computing devices

Fondazione Bruno Kessler (FBK)

Location

At user's premises

Italy

Remote

Arable farming

Food processing

Greenhouse

Horticulture

Livestock farming

Tree Crops

Viticulture

The tests in this service are aimed at products that run complex apps (based on a multitude of microservices, including heavy AI algorithms) in the fields, with limited to no connectivity to the cloud. In these cases, we propose a benchmarking designed to understand what the bottlenecks are in the implemented solution architecture to help TEF customers improve the efficiency and reliability of such products/solutions. The service may also encompass virtual facility elements (enriched datasets, virtual machines hosting containerised modules) blended with physical sensors and infrastructure in our test field or directly installed at the client's premises if need be for the duration of the experimentation.

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

Danish Technological Institute (DTI)

Location

Denmark

Remote

Arable farming

Food processing

Greenhouse

Horticulture

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.

Blockchain in agriculture traceability systems

Università degli Studi di Napoli Federico II

Location

Italy

Arable farming

Food processing

Greenhouse

Horticulture

Livestock farming

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.

Cold chain monitoring

Università degli Studi di Napoli Federico II

Location

Italy

Arable farming

Food processing

Greenhouse

Horticulture

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.

Collection of test data during digital testing

Politecnico di Milano (POLIMI)

Location

Italy

Remote

Arable farming

Food processing

Greenhouse

Horticulture

Livestock farming

Tree Crops

Viticulture

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 during physical testing

Politecnico di Milano (POLIMI)

Location

Italy

Arable farming

Food processing

Greenhouse

Horticulture

Livestock farming

Tree Crops

Viticulture

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. 

Combating forgery in the agri-food chain

Università degli Studi di Napoli Federico II

Location

Italy

Arable farming

Food processing

Greenhouse

Horticulture

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.

Custom AI-Optimized Testing Protocol Design for Agricultural Solutions

Danish Technological Institute (DTI)

Location

Denmark

Arable farming

Greenhouse

Horticulture

Livestock farming

We specialise in designing comprehensive testing protocols for the physical testing of customer solutions in the agricultural sector. Our approach focuses on creating consistent procedures that enhance the precision of AI models developed from the collected data. These protocols are carefully crafted to meet individual customer and project needs, incorporating requirements for both laboratory and field testing scenarios. We prioritise automation in data collection to ensure consistent and repeatable testing procedures, which is crucial for high-quality data collection. This service bridges the gap between physical testing and AI model development, providing a solid foundation for advancing agricultural technologies.

Data Collection for Advanced Agricultural Environmental Monitoring and Analysis Systems

Danish Technological Institute (DTI)

Location

Denmark

Arable farming

Greenhouse

Horticulture

Livestock farming

We offer cutting-edge technical systems, both hardware and software, along with expert knowledge for collecting and analysing relevant environmental data during agricultural testing. Our service emphasises the use of standardised protocols to ensure enhanced accuracy and efficiency. We provide a comprehensive suite of monitoring and analysis tools, including methane detection systems, PTR-MS for volatile organic compound analysis, GHG measurement using CRDS technology, and automated climate data collection using IoT sensors and AI algorithms. These advanced systems enable precise environmental monitoring and data analysis for agricultural research and development.

Data Collection using UGVs and modular sensor payloads

Fondazione Bruno Kessler (FBK)

Location

At user's premises

Italy

Arable farming

Greenhouse

Horticulture

Tree Crops

Viticulture

This service consists of deploying a modular sensor payload (equipped with Lidar, inertial motion units, GNSS, Radar, stereo cameras, and ultra-wideband technology) to agricultural environments such as vineyards and orchards to collect and annotate high-fidelity datasets tailored to customer needs. Data are systematically labelled and can be delivered to agrifoodTEF customers as structured datasets for AI-driven agricultural solutions.