Early detection of common reed ( Phragmites australis ) using unoccupied aerial vehicles and deep learning

The combination of unoccupied aerial vehicles and deep learning offers a promising approach for mapping invasive alien plant species (IAPS), though its effectiveness in early detection case remains uncertain. In this study, we evaluated the suitability of this approach based on a convolutional neural network for mapping the location of common reed (Phragmites australis subsp. australis) within Parc national des Îles-de-Boucherville located in southern Québec, Canada. We collected data on six distinct dates (July-October 2022) during the growing season, covering environments with different levels of reed invasion (Dense, Establishing and Post-treatment). Overall, model performance was high for the different dates and zones, especially for recall (mean of 0.89). The results showed an increase in performance, reaching a peak following the appearance of the inflorescence in September (highest F1-score at 0.98). Despite challenges associated with common reed mapping in a post-treatment monitoring context with poorer detection, this approach has the potential to serve as an effective tool for speeding up the work of biologists in the field and ensuring better management of IAPS. To this end, we provide a comprehensive dataset of high-resolution imagery and deep learning models enabling the detection of common reed across its phenological cycle.