Catalogue of Services

Are you looking for a service to validate, test or evaluate your agrifood product? 
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AGRIFood Catalogue services
  • 106 results found
Test and experimentation of robotic and AI solutions in field crops
Wageningen Research - WR
Location
Netherlands
Arable farming
Horticulture
Tree Crops

The core business of Wageningen Research - Field Crops is to design and create experimental plots (e.g. at the Farm of the Future) and to collect objective data on crops, weeds, diseases, pests and quality and effectiveness of robotic and AI solutions, in which we provide statistical reliable datasets. Examples of experiments are e.g. testing the accuracy and quality of handsfree harvesting solutions, weeding robots and path following of autonomous implement carriers in field crops. We offer for example the following measurements : -Provide measurements on effectiveness of solutions on control of weeds, pests and diseases (e.g. effectiveness of a weed robot) -Provide timeseries of vegetation indices (drone) -Provide yield measurements (quantity and quality) -Provide plant measurements (emergence, flowering, senescence) -Provide weed countings (including species determination) -Provide sensor data (soil moisture, insect monitoring, etc.) Furthermore we have common agricultural machines and implements available. We can cover different field crops, but we also are specialized in new crop farming systems that are based on agro-ecological principles. Think of strip and mosaic farming. Get in touch to discuss the possibilities for your experiment or test question.

Collection of test data
Performance evaluation
Provision of datasets
Test design
Test execution
Test setup
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
Test execution
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
Test execution