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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

Testing and evaluating sensor devices

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

Testing and evaluating the robot’s functionalities

The SOPHIA infrastructure will offer the possibility to test and evaluate the performance of the robot’s functionalities in a locally controlled environment. While our indoor facility will offer a fully controlled environment (light, volume), all facilities at SOPHIA can be customised to test and evaluate the robot’s functionalities. The service consists of three main steps. The robot will be integrated into the facility. After that, the facility will be customised according to the functionality of the robot. Then, the performance of the robot’s functionalities will be tested and evaluated with quantitative and qualitative metrics.

AI model training

Collection of test data

Data analysis

Desk assessment

People training

Performance evaluation

Test design

Test execution

Test setup

Testing and evaluation of mobility algorithms with ground robots

The SOPHIA infrastructure provides the ability to test and evaluate mobility algorithms embedded on a ground robot. Mobility algorithms concern the classical robotics functionalities of mapping, localisation, SLAM, and navigation. The ground robot is equipped with an array of sensors, including a camera, LiDAR, IMU, and RTK-GPS for ground truth evaluation. The service proceeds in three stages. Firstly, we evaluate the algorithm using representative datasets. After that, the algorithm is integrated into a ROS2 architecture and evaluated with the local agrifoodTEF test infrastructure (various areas are possible). The performance of different attributes of the algorithm is assessed using quantitative and qualitative metrics. Benchmarking could be proposed as a complementary option to position the performance of the proposed algorithm in relation to the current state of the art. The final step involves field testing under real conditions at a specific end-user or customer site using the mobile living lab, which consists of a mobile laboratory deployed in the field and connected to the real robot for monitoring and evaluation purposes.

Collection of test data

Data analysis

Desk assessment

Performance evaluation

Test design

Test execution

Test setup

Testing and evaluation of mobility algorithms with aerial robots

The SOPHIA infrastructure will offer the possibility to test and evaluate the mobility algorithms embedded on an aerial robot. Mobility algorithms concern the classical robotics functionalities of mapping, localisation, SLAM, and navigation. The aerial robot is equipped with an array of sensors, including a camera, LiDAR, IMU, and RTK-GPS (for ground truth evaluation). The service consists of three main steps. To begin with, the algorithm is evaluated using representative datasets. After that, the algorithm is integrated into a ROS2 architecture and evaluated with the local agrifoodTEF test infrastructure (various areas are possible). The performance of different attributes of the algorithm is evaluated using quantitative and qualitative metrics. Benchmarking could be proposed as a complementary option to position the performance of the proposed algorithm in relation to the current state of the art. The final step involves field testing under real conditions at a specific end-user or customer site using the mobile living lab (which consists of a mobile laboratory deployed in the field and connected to the real robot for monitoring and evaluation purposes).

Collection of test data