Catalogue of Services

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AGRIFood Catalogue services
  • 34 results found
Testing and evaluation of mobility algorithms with 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

The sophia infrastructure will offer the possibility to test and evaluate the mobility algorithms embedded on a ground robot. M

obility Algorithms concerns the classical robotics functionalities of Mapping, Localization, SLAM, and Navigation.

The aerial robot is equipped with an array of sensors, including Camera, LiDAR, IMU, and RTK-GPS (for the ground truth evaluation). The service consists of three main steps:

  1. First of all, the algorithm is evaluated using representative datasets. 
  2. After that, the algorithm is integrated in a ROS2 architecture and evaluated with the local agrifoodTEF test infrastructure (different areas are possible). The performance of different attributes of the algorithm is evaluated with quantitative and qualitative metrics. A possibility of benchmarking will be proposed as a complementary option in order to position the performance of the proposed algorithm regarding the current state of the art. 
  3. Finally, the last step will be to perform the field testing in real condition and in a particular end-user or customer site using the mobile living lab (it consists of a mobile laboratory going to the field connected with the real robot for monitoring and evaluation purposes).

AI model training
Collection of test data
Data analysis
Desk assessment
People training
Performance evaluation
Provision of datasets
Test design
Test setup
Text execution
Testing and evaluation of mobility algorithms with ground robot
National Institute for Research in Digital Science and Technology  - INRIA
Location
At user's premises
France
Arable farming
Food processing
Greenhouse
Horticulture
Tree Crops
Viticulture

The sophia infrastructure offers the possibility to test and evaluate the mobility algorithms embedded on a ground robot. 

Mobility Algorithms concerns the classical robotics functionalities of Mapping, Localization, SLAM, and Navigation. The ground robot is equipped with an array of sensors, including Camera, LiDAR, IMU, and RTK-GPS (for the ground truth evaluation). 

The service consists of three main steps:

  1. First of all, the algorithm is evaluated using representative datasets. 
  2. After that, the algorithm is integrated in a ROS2 architecture and evaluated with the local agrifoodTEF test infrastructure (different areas are possible). The performance of different attributes of the algorithm is evaluated with quantitative and qualitative metrics. A possibility of benchmarking will be proposed as a complementary option in order to position the performance of the proposed algorithm regarding the current state of the art. 
  3. Finally, the last step will be to perform the field testing in real condition and in a particular end-user or customer site using the mobile living lab (it consists of a mobile laboratory going to the field connected with the real robot for monitoring and evaluation purposes). 

AI model training
Collection of test data
Data analysis
Desk assessment
People training
Performance evaluation
Provision of datasets
Test design
Test setup
Text execution
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.

AI model training
Collection of test data
Data analysis
Data augmentation
Performance evaluation
Provision of datasets
Test design
Test setup
Text execution
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. 

AI model training
Collection of test data
Data analysis
Data augmentation
Desk assessment
Performance evaluation
Provision of datasets
Test design
Test setup
Text execution