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Evaluation of the energy efficiency of the embedded hardware associated with the AI functionality

Laboratoire National de Métrologie et d’Essais - LNE

By having your AI or robotic system tested by LNE, you ensure it meets the highest standards of safety and performance, boosting your product’s reliability and trustworthiness in the market. Partnering with LNE for rigorous testing of your robotic and AI technologies gives you a competitive edge and trusted third-party assessment that enhances your product credibility and opens doors to new market opportunities both locally and globally. This service provides testing of devices with AI-integrated control systems, focusing on energy efficiency and consumption during the product life cycle. By simulating a wide range of scenarios, we provide detailed insights into the energy performance of these AI systems with regard to their expected operational environment. These tests can be conducted under different conditions, either in a completely simulated way, through a hybrid test bench where the actual physical device is assessed within a simulated environment, or using physical infrastructures based on the device's operational environment to test it in a 'real' setting. Tests are conducted under controlled laboratory conditions, ensuring optimal reproducibility and repeatability, providing reliable insights into the system's efficiency and safety-critical functionalities. Typical assessments may cover the energy efficiency of SLAM, various types of end effectors, and other relevant components such as sensors, motors or AI software. This rigorous testing process helps identify potential areas for improvement, enabling companies to enhance the efficiency and sustainability of their products, ultimately leading to cost savings and reduced environmental impact.

Performance evaluation

Test design

Test execution

Test setup

Execution of digital testing

Politecnico di Milano

This service assists customers in executing digital tests using agrifoodTEF’s digital infrastructure. Tests can either be defined by the customer or designed through agrifoodTEF services (e.g., S00176, S00177, S00178). If specific activities are required to properly interface the system under test with the digital infrastructure, these can be handled by either the customer or agrifoodTEF (via Service S00180).

Support provided by this service includes general assistance during the test execution phase, such as monitoring to ensure compliance with the testing protocol or offering immediate help in identifying and resolving issues. Additionally, the service provides technical support during the test execution of the system under test, including ensuring that all necessary processes are correctly configured and running, as well as helping the customer address any unforeseen challenges that may arise when complex digital systems interact.

Finally, through this service, agrifoodTEF manages the technical infrastructure for test execution, ensuring smooth data exchange between the system under test and the agrifoodTEF infrastructure, and verifying that all testing requirements—potentially identified during the desk assessment phase (Service S00179)—are met during the actual test runs.

Test execution

Field Spectrometer Measurements

Instituut voor Landbouw-, Visserij- en Voedingsonderzoek

This service utilises field spectrometers to measure the spectral properties of physical environments, such as barns, greenhouses, or outdoor fields. By capturing detailed spectral data, we help you develop, improve, or validate your agri-food solutions, whether for monitoring plant health, optimising lighting conditions, or enhancing environmental control systems. The measurements provide insights into how different wavelengths of light interact with the environment, enabling fine-tuned adjustments for better crop growth, energy efficiency, or precision farming applications. Additionally, this service can serve as a follow-up to lab-scale measurements where hyperspectral technology is used to conduct pilot tests to determine which wavelengths provide valuable information. In this service, we then perform field tests on these specific wavelengths.

Collection of test data

Conformity assessment

Desk assessment

Performance evaluation

Provision of datasets

People training services

Politecnico di Milano

The AgrifoodTEF consortium comprises a set of testing and experimentation facilities and targeted services aimed at supporting customers in the testing and validation of systems and devices based on advanced technologies. Given the technical complexity of these facilities and services and the associated complexity of many activities making use of them, many customers can benefit from a preliminary training activity. The goal of such training is to make best use of the facilities and services and fully exploit the allotted time when actual testing/experimentation occurs. Customer training – involving both technical and procedural aspects – can either be sought by the customer autonomously leveraging the documentation provided by AgrifoodTEF or directly supported by AgrifoodTEF via this service; a combination of both approaches is also possible. This service does not limit its applicability to customers that already know what they require from AgrifoodTEF. The service also provides an opportunity for customers to discuss their specific needs and requirements with our team and be directed towards the set of services in the catalogue that best suit their needs. Especially where the customer envisages an articulate testing campaign, exploring their idea together with AgrifoodTEF via this service can be very beneficial in planning subsequent activities. This service can, if needed, be integrated with services S00109 (desk assessment activities for physical systems) or S00179 (desk assessment activities for digital systems or data) to anchor its operation and possible further testing activities more closely to the specific features of the system(s) developed by the customer.

People training

Performance evaluation of AI and robotics based on physical testing environments

Laboratoire National de Métrologie et d’Essais - LNE

Performance evaluation

Test design

Test execution

Test setup

Preparation of computational test environment

Politecnico di Milano

To successfully conduct a digital testing campaign, preparatory activities are usually required to set up the computational environment used for testing. This service performs activities such as: - setting up the hardware resources needed to run the tests - configuring and initialising the virtual environments used for testing (e.g., Docker images, private “data rooms” for safe data sharing) - installation and configuration of required software packages and dependencies - Setting up authentication layers, user roles and credentials as needed- Migration and/or exchange of required data and ground truth annotations - installation and configuration of a simulator - importing and configuring a previously defined simulated environment - executing dry runs to check that all elements of the test environment operate as required Environment preparation is done according to an environment design provided by the customer. If needed, such design can be done by AgrifoodTEF for the customer via Service S00176. Interested customers can get support from AgrifoodTEF for the entire pipeline involved in digital testing, from the design of its other elements beside the environment (namely, the testing protocol via service S00177 and the evaluation metrics via service S00178) to test execution (service S00182) and data collection (service S00183) and evaluation (S00184). Support in interconnecting the systems under test to the digital testing environment, if needed, is available via service S00181.

Test setup

Provision of datasets

Politecnico di Milano

During the development of systems based on AI and/or robotics, tailored data are needed to support the realisation, validation and optimisation of systems and subsystems. This service aims at providing such data. AI models are a noteworthy example of systems that require data to be trained; more generally, any system which processes data produced via artificial perception (such as images, videos, LiDAR, ultrasound, radar...) requires suitable data to be built, tested and fine-tuned. This service allows a customer to define together with AgrifoodTEF the specific features of the data that the customer’s activities require and to receive datasets precisely matching such features. Among the features to be defined are the type, number and specifications of the data streams comprised in the datasets (e.g., when multi-sensor data are required) and the range of variations encompassed by the data (e.g., variety of environmental conditions represented in a sensor stream); lower-level features to be defined concern formatting and packaging of the data.

Provision of datasets

Provision of datasets for AI training in real vine-growing conditions

Institut Français de la Vigne et du Vin - IFV

Location

France

Viticulture

If you already have an AI-based project in the viticulture field but need more data to enhance your model, we can provide you with tailor-made datasets thanks to our experimental vineyard and its great diversity in terms of grape varieties, soil conditions, and moisture at different periods of the canopy growth. Datasets can concern vines, grapes, juices, and even wine.

AI model training

Data augmentation

Provision of datasets

Provision of dedicated and private experimentation facilities on a real vineyard

Institut Français de la Vigne et du Vin - IFV

Location

France

Viticulture

Install your robotic or AI solution on a real vineyard to assess their performance or to foster their development with storage and workshop facilities

Performance evaluation

Provision of general-purpose datasets via multisensory ground robot

National Institute for Research in Digital Science and Technology  - INRIA

General-purpose datasets serve two primary objectives: (i) evaluating mobility algorithms and (ii) developing and assessing general-purpose AI applications. In the context of mobility algorithms, this pertains to classical robotics tasks such as mapping, localisation, SLAM (Simultaneous Localisation and Mapping), and navigation. Meanwhile, general-purpose AI applications focus on advancing algorithms and feeding decision support systems (DSS) for tasks such as, but not limited to, weed detection, health monitoring, growth and maturity assessment, and yield estimation in areas like arable farming, horticulture, food processing, forestry, and tree management. A significant challenge in developing AI solutions for agricultural robotics lies in the dynamic nature of agricultural environments, which fluctuate with different seasons and weather conditions. To address this, acquiring consistent and periodic data is essential for monitoring these changes effectively. This real-time data collection, often facilitated by ground robots, is crucial for developing efficient algorithms and AI solutions. Such datasets can support the development of sensor-specific techniques or be leveraged to create multisensory algorithms, enabling more accurate and adaptable systems for agricultural applications.

Data analysis

Data augmentation

Desk assessment

Provision of datasets

Provision of general-purpose datasets via a multisensory aerial robot.

National Institute for Research in Digital Science and Technology  - INRIA

We provide general-purpose datasets that can be used by customers to evaluate mobility algorithms and to develop and assess general-purpose AI applications. In the context of mobility algorithms, this pertains to classical robotics tasks such as mapping, localisation, and SLAM (Simultaneous Localisation and Mapping). Meanwhile, general-purpose AI applications focus on advancing algorithms and feeding decision support systems (DSS) for tasks including, but not limited to, weed detection, health monitoring, growth and maturity assessment, and yield estimation in areas such as arable farming, horticulture, food processing, forestry, and tree management. A significant challenge in developing AI solutions for agricultural robotics lies in the dynamic nature of agricultural environments, which fluctuate with different seasons and weather conditions. To address this, acquiring consistent and periodic data is essential for effectively monitoring these changes. This real-time data collection, often facilitated by aerial robots, is crucial for developing efficient algorithms and AI solutions. Such datasets can support customers in the development of sensor-specific techniques or be leveraged to create multisensory algorithms, enabling more accurate and adaptable systems for agricultural applications.

Data analysis

Data augmentation

Desk assessment

Provision of datasets

Provision of general-purpose datasets with user-specified sensor(s)

National Institute for Research in Digital Science and Technology  - INRIA

General-purpose datasets serve two primary objectives: (i) evaluating mobility algorithms and (ii) developing and assessing general-purpose AI applications. In the context of mobility algorithms, this includes classical robotics tasks such as mapping, localisation, SLAM (Simultaneous Localisation and Mapping), and navigation. Meanwhile, general-purpose AI applications focus on advancing algorithms and supporting decision support systems (DSS) for tasks such as, but not limited to, weed detection, health monitoring, growth and maturity assessment, and yield estimation in areas like arable farming, horticulture, food processing, forestry, and tree management. A significant challenge in developing AI solutions for agricultural robotics lies in the dynamic nature of agricultural environments, which fluctuate with different seasons and weather conditions. To address this, acquiring consistent and periodic data is essential for effectively monitoring these changes. This real-time data collection, often facilitated by aerial and/or ground robots equipped with user-specified sensors, is crucial for developing efficient algorithms and AI solutions. Such datasets can support the development of sensor-specific techniques or be leveraged to create multisensory algorithms, enabling more accurate and adaptable systems for agricultural applications.

Data analysis

Data augmentation

Desk assessment

Provision of datasets

Catalogue of Services

All agrifoodTEF services can be customised!