Mapping indicator species of segetal flora for result-based payments in arable land using UAV imagery and deep learning

Abstract

The decline of segetal flora species across Europe, driven by intensified agricultural practices, is impacting other taxa and ecosystem functions. Result-based payments to farmers offer an effective solution to conserve these species, but the high cost of biodiversity monitoring remains a challenge. In this study, we conducted UAV flights with an RGB camera and used the deep learning model YOLO to detect these species in four winter barley fields under different management intensities in Germany. Field measurements of plant traits were used to evaluate their impact on species detectability. Additionally, we investigated the potential of spatial co-occurrence and canopy height heterogeneity to predict the presence of species difficult to detect by UAVs. We found that half of the species observed could be remotely detected, with a minimum ground sampling distance (GSD) of 1.22 mm required for accurate annotation. The same detection ratio was estimated for key indicator species not present in our study area based on trait information. Plant height was crucial for species detection, with accuracy ranging between 49–100 %. YOLO models effectively predicted species from images taken at 40 m, reducing the monitoring time to eight minutes per hectare. Co-occurrence with UAV-detectable species and canopy height heterogeneity proved promising for identifying areas where undetectable species are likely to occur, although further research is needed for landscape-level applications. Our study highlights the potential for large-scale, cost-effective monitoring of segetal flora species in agricultural landscapes, and provides valuable insights for developing robust ‘smart indicators’ for future biodiversity monitoring.