Catalogue of Services

Provision of datasets for simulation and visualisation

Wageningen Research - WR

Location

Netherlands

Remote

Arable farming

Horticulture

Tree Crops

In order to develop digital twins for agriculture, reliable long-term datasets on the agronomy and ecology of fields are crucial. From several long-term experiments and agricultural systems (like the Farm of the Future), datasets of fields are available with registrations of crop operations, crop measurements (e.g., timeseries of vegetation indices), yield, soil, & weather data, and data on key performance indicators. In some cases, other sensor data (tractor data, soil moisture, insect monitoring) are also available. New datasets can be developed based on specific needs.

Provision of datasets for validation of AI algorithms for livestock farming

Wageningen Research - WR

Location

Netherlands

Remote

Livestock farming

Thanks to 30 years of data gathering at Agro-innovation Centre De Marke, multiple data sets are available. During these years, data is gathered on individual cow events, barn emissions, yields of specific and typical livestock farm crops, soil samples were taken, and feeding strategies were developed. Due to the systematic approach of De Marke, this data can be easily reused for setting up specific validation of AI algorithms. Additionally, new data can be gathered as requested. 

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  

Provision of general purpose datasets via multisensored ground robot

National Institute for Research in Digital Science and Technology  - INRIA

Location

France

Arable farming

Food processing

Greenhouse

Horticulture

Tree Crops

General-purpose datasets serve two primary objectives: 

1. Evaluating mobility algorithms;
2. Developing and assessing general-purpose AI applications. 

In the context of mobility algorithms, this pertains to classical robotics tasks such as mapping, localization, SLAM (Simultaneous Localization and Mapping), and navigation. 

Meanwhile, general-purpose AI applications focus on advancing algorithms and feeding decision support systems (DSS) for tasks like 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. 

Provision of general purpose datasets via multisensored aerial robot

National Institute for Research in Digital Science and Technology  - INRIA

Location

France

At user's premises

Arable farming

Food processing

Greenhouse

Horticulture

Tree Crops

Viticulture

General-purpose datasets serve two primary objectives: 

1. Evaluating mobility algorithms;
2. Developing and assessing general-purpose AI applications. 

In the context of mobility algorithms, this pertains to classical robotics tasks such as mapping, localization, SLAM (Simultaneous Localization and Mapping), and navigation. 

Meanwhile, general-purpose AI applications focus on advancing algorithms and feeding decision support systems (DSS) for tasks like 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 aerial 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. 

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

National Institute for Research in Digital Science and Technology  - INRIA

Location

France

At user's premises

Arable farming

Food processing

Greenhouse

Horticulture

Tree Crops

Viticulture

General-purpose datasets serve two primary objectives: 

1. Evaluating mobility algorithms;
2. Developing and assessing general-purpose AI applications. 

In the context of mobility algorithms, this pertains to classical robotics tasks such as mapping, localization, SLAM (Simultaneous Localization and Mapping), and navigation. Meanwhile, general-purpose AI applications focus on advancing algorithms and feeding decision support systems (DSS) for tasks like 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 aerial and/or ground robots equipped with user specified sensor(s), 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.

Provision of raw image datasets in field crops

Wageningen Research - WR

Location

Netherlands

Arable farming

Horticulture

Tree Crops

In order to develop AI applications or vision functionalities in robots, one of the first steps is to have high-quality images of crops, weeds, nutrient deficiencies, diseases, or pests available. As the phenotype of plants can really differ between regions and seasons, providing balanced datasets with high-quality images and different illumination could save valuable (development and acquisition) time. The data can be used for annotation purposes or to have a first visual check if an existing trained algorithm works sufficiently. 

Provision of support and expertise for robotic developments and testings in vineyards

Institut Français 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.

Real-Time Data Integration for Smart Irrigation Systems

HISPATEC

Location

At user's premises

Arable farming

Greenhouse

Horticulture

Tree Crops

Viticulture

This experimental service focuses on establishing a real-time data connection for irrigation systems, enabling precise adjustments in water management based on soil moisture levels, weather forecast, and plant needs. The service offers real-time analysis and decision-making tools, enhancing irrigation system efficiency and ensuring optimal resource utilization. It includes comprehensive testing and evaluations to ensure smooth integration and optimal performance under various conditions.

Satellite-based models for precision agriculture

University of Cordoba

Location

Spain

Arable farming

This service provides testing and experimentation support for satellite-based models aimed at yield estimation and pre-harvest assessments. By leveraging remote sensing data, clients can develop and validate models that predict agricultural outputs, enhancing decision-making processes in arable farming. This service is ideal for clients seeking to integrate precision agriculture techniques using satellite imagery to optimise crop management and planning.

Satellite: Evaluation of results of physical testing

University of Cordoba

Location

Remote

Spain

Arable farming

Horticulture

Livestock farming

This service offers an evaluation of satellite-derived data quality by applying performance metrics that assess model reliability and effectiveness. The service includes a comprehensive analysis of test data and previously collected ground truth data to validate the performance of models used in precision agriculture. With a detailed performance report, clients receive valuable insights that support model improvement and informed decision-making in agricultural management.

Technology Setup and Integration for Agricultural Production Testing

HISPATEC

Location

At user's premises

Arable farming

Greenhouse

Horticulture

Tree Crops

Viticulture

This experimental service focuses on the initial setup and integration of new technologies within the client’s production environment. The process includes connecting hardware and software systems, configuring settings, and ensuring that the technology is fully operational and integrated with existing workflows. The integration is performed with minimal disruption to daily operations and includes tests to ensure compatibility and optimal performance from the start.