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Validation of Smart Irrigation Technologies

This service offers agrotech companies the opportunity to test and validate the results of smart irrigation technologies in real farming environments. The service includes installing the technology on-site and monitoring its performance under real-world conditions. Metrics such as water usage, crop yield, and system efficiency are analysed to provide a comprehensive validation of the technology's impact. This hands-on evaluation ensures that companies can implement these systems with confidence, knowing their effectiveness in the field.

Collection of test data

Performance evaluation

Test execution

Validation of Weed Detection Systems

Universitat de Lleida (UdL)

This service evaluates the accuracy and effectiveness of AI-driven weed detection systems, ensuring their accuracy in real agricultural conditions. By combining field trials (both in experimental and real-farm fields), drone-based imaging, and ground truth validation, the service assesses the system’s ability to detect weeds with high precision, minimising false positives (incorrect weed detection) and false negatives (missed weeds). The service also validates the system's adaptability to different environmental conditions, such as varying soil types, weather, and crop growth stages, and it is applicable to herbaceous and arable crops, offering valuable insights for improving targeted weed control and reducing unnecessary herbicide use.

Collection of test data

Performance evaluation

Provision of datasets

Test design

Test execution

Test setup

Validation of Yield Estimation based on Computer Vision

Universitat de Lleida (UdL)

Validation of fruit detections, diameter estimation, and yield estimates using artificial intelligence, in particular computer vision techniques.

The service will help the solution provider check its performance using reference datasets or experiments conducted in testing fields. The service might also include the generation of reference datasets and expected detection, diameter measures, or yield values, which can be used both for validation and training of the artificial intelligence solution based on computer vision.

AI model training

Collection of test data

Performance evaluation

Test design

Test execution

Test setup