Automatic Detection of Ceroxylon Palms by Deep Learning in a Protected Area in Amazonas (NW Peru)

Abstract

Habitat fragmentation and loss seriously threaten Ceroxylon palms, a key and vulnerable species in Andean forests. Given the need for efficient tools for their monitoring and conservation, this study aimed to evaluate the effectiveness of deep learning YOLO models for the automatic detection of Ceroxylon individuals in high-resolution UAV images. Three versions of YOLO (v8, v10, and v11) were analyzed, each in nano (“n”), medium (“m”), and extra-high (“x”) configurations, considering both processing time and detection accuracy. Difficulties in orthomosaic reconstruction were addressed by specific adjustments to the photogrammetric software parameters. The nine resulting models were tested in seven study plots, with the YOLOv8-m configuration standing out as the one that best balanced processing speed and accuracy, achieving the following outstanding metrics: F1 = 0.91; mAP50 = 0.98; and mAP50-95 = 0.62. These results demonstrate the practical value of YOLO model automatic detection for the informed management and effective conservation of Ceroxylon in mountain ecosystems.