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Livestock Dataset Generation

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

Location

Belgium

Livestock farming

This service supports the generation of high-quality datasets tailored for validating and improving AI-driven technologies or sensors designed for livestock management. Whether you are developing new tools for monitoring, controlling, or working with livestock, we help collect and process relevant data that reflects real-world conditions. These datasets enable you to fine-tune your AI models and sensor systems, ensuring they operate accurately and effectively. By leveraging this service, you can optimise your solutions for livestock health monitoring, behaviour analysis, and automated control systems in the agri-food sector.

Collection of test data

Desk assessment

Performance evaluation

Provision of datasets

Test design

Test execution

Test setup

Performance evaluation of AI and robotics based on physical testing environments

Laboratoire National de Meterologie 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, as our testing report enhances credibility and opens doors to new market opportunities both locally and globally. Testing of devices with AI-integrated control systems by our LE.IA facility focuses on both performance and performance-related safety. Our tests, conducted under controlled laboratory conditions, ensure optimal reproducibility and repeatability, providing reliable insights into the system's efficiency and safety-critical functionalities.

Performance evaluation

Test design

Test execution

Test setup

Policy lab for innovative drone technologies in regulatory sandboxes

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

This service is designed to support agri-food companies that want to test and evaluate the use of innovative drone technologies in a controlled environment—a so-called regulatory sandbox. Setting up such a drone sandbox requires navigating complex airspace regulations, flight safety rules, environmental constraints (e.g., drift, pesticide use), and data protection standards. Our experts help you design and prepare a sandbox operation from A to Z: from selecting a test location and identifying legal permissions needed to preparing risk assessments, notifying authorities, and ensuring safe operational procedures. We also offer advice on sensor integration, traceability, and aligning with EU drone and agricultural regulations such as Regulation EU 2019/947 on the operation of unmanned aircraft. This is an ideal service for companies wishing to run trials with crop spraying or precision seeding drones before launching commercially. - Set up a regulatory sandbox to safely and legally test spraying or seeding drones. - Understand the full list of permissions, safety protocols, and risk assessments required. - Learn how to engage with authorities (e.g., airspace regulators, pesticide control bodies). - Get support in preparing the documentation to operate drones legally during the test phase.

Desk assessment

Test design

Test setup

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

Institut Francais 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 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

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 support and expertise for robotic developments and testings in vineyards

Institut Francais de la Vigne et du Vin (IFV)

Location

France

Viticulture

You will have access to feedback on your prototype or about your project, technical and economical references, and a global approach on the use-case level. You can also access vineyards and other facilities to try different options and get more feedback. Finally, you will have access to focus groups with viticulturists to validate your choices.

Test design

Test execution

Test setup

Qualification of Intelligent Weeding Technologies

The AgroTechnoPôle, under the leadership of INRAE-TSCF, has pioneered a groundbreaking testing facility designed to assess alternative weeding technologies. This innovative test bench accurately simulates various crop and weed scenarios—including maize, wheat, beans, mustard, and chamomile—allowing for realistic evaluations. By replicating real-world conditions, we can effectively analyse the performance of weeding systems in a controlled and reproducible environment, ensuring the development of more efficient agricultural practices. With this innovative test bench, we can critically assess the spatial precision of perception and decision-making systems. It comprehensively analyses the effectiveness of diverse solutions that integrate perception, decision-making, and action, whether through mechanical, laser, electrical, or targeted spraying weeding methods. The tests occur at AgroTechnoPôle (INRAE—Montoldre, France) and last 2 to 4 weeks, depending on the technology to be tested.

Performance evaluation

Test execution

Test setup

Qualification of trajectory execution accuracy of crop production robots (outdoor mobility)

The test assesses the accuracy of crop production robots in following precise paths across different terrains, simulating typical agricultural tasks. The test uses advanced laser tracking to measure how well the robot follows a set trajectory, including straight and curved paths, on slopes and amidst obstacles. This enables manufacturers to ensure that their robots meet performance standards for real-world field operations. A detailed report is provided, helping optimise robot performance for outdoor mobility in agricultural settings. A manufacturer’s representative must oversee the process. This test is part of a series known as ARPA: Agricultural Robot Performance Assessment. It is identified as ARPA n (where n is 1, 2, 3, etc.) for tests that focus on safety functions and as ARPA XXn for other performance evaluations.

Collection of test data

Performance evaluation

Test design

Test execution

Test setup

Qualification of trajectory execution accuracy of livestock robots

ARPA PC2 thoroughly evaluates how well agricultural robots can follow established paths in indoor and outdoor environments. This service helps ensure that robots can successfully navigate various settings typical of a farm, including any obstacles and transitions between spaces they might face. ARPA PC2 provides essential information about the robot's accuracy and behaviour while moving by testing in real-life conditions. This assessment enhances operational reliability and safety and supports manufacturers in optimising their robots for improved performance in diverse agricultural scenarios.

Collection of test data

Performance evaluation

Provision of datasets

Test design

Test execution

Test setup

Testing and evaluating sensor devices

National Institute for Research in Digital Science and Technology  (INRIA)

The SOPHIA infrastructure will offer the possibility to test and evaluate the performance of a sensor device in a locally controlled environment. While our indoor facility provides a fully controlled environment (light, volume), all facilities in SOPHIA can be customised to test and evaluate the sensor device. The service consists of three main steps. First, the sensor device will be integrated into the facility. After that, the facility will be customised according to the final application of the sensor device. Then, the performance of the sensor device will be evaluated using quantitative and qualitative metrics. Comparison could be proposed as a complementary option to position the performance of the sensor device in relation to the current state of the technology available in SOPHIA.

Collection of test data

Data analysis

Desk assessment

People training

Performance evaluation

Provision of datasets

Test design

Test execution

Test setup