Tree invasions are a serious threat to grassland ecosystems, but control measures often rely on diagnostic approaches that are not yet effective or scalable. This study applied the deep learning algorithm Mask R-CNN to detect an invasive exotic species (Pinus elliottii) in a native wetland area, aiming to create a biological invasion diagnostic tool to support the management of native areas and assist in biological invasion control. The model was developed using high spatial resolution images (1.5 cm/pixel) and achieved a mean Average Precision (mAP) of 78 % and an Intersection over Union (IoU) score of 81 %. The segmentations generated by the model provides an assessment of the biological invasion process caused by Pinus spp. through the detection of individual trees, quantify the canopy cover area affected and evaluate the effectiveness of the method as a supporting tool for biodiversity conservation in protected areas. Recognizing the management priority of invasive exotic species and the limitations of available tools for public managers, the deep learning approach presented here may contribute to the development of diagnostics that inform more targeted and effective management actions, reducing financial costs, environmental impacts, and time spent on field activities.