Abstract. Araucaria angustifolia (Bertol.) Kuntze, an iconic and endemic species of the Mixed Ombrophilous Forest, plays a key ecological and economic role within the Atlantic Rainforest. However, it is currently threatened by historical overexploitation and its sensitivity to climate change. This study examines the application of deep learning for the automated detection of A. angustifolia individuals in high-resolution imagery collected by Unmanned Aerial Vehicle (UAV) at selected sites in Santa Catarina, Brazil. The YOLOv11x model, a state-of-the-art convolutional neural network (CNN) architecture, was trained using two distinct datasets: a heterogeneous set and a more homogeneous one, the latter evaluated with K-Fold cross-validation. Results showed that model performance improved with increased data uniformity, with average precision (AP) rising from 21% to 27% and the F1-score from 54% to 61%. While detection accuracy remains below optimal levels, the findings highlight the model's potential for species identification. Enhancements in annotation quality, dataset diversity, and hyperparameter optimization are recommended to improve performance further and support more robust monitoring and conservation efforts for A. angustifolia.
Mendes et al. (Fri,) studied this question.