Sep 21, 2026, cs.CVJ/K move · Enter open · S save
Reza Saputra, Diah Harnoni Apriyanti, André Schuiteman, Kurt Metzger+3
College of Science and Engineering, James Cook University. McGregor Rd, Smithfield, Cairns, QLD 4878, Australia · Southwest Papua Natural Resources Conservation Agency, Ministry of Forestry, Indonesia. Jalan Klamono KM 16, Sorong, Southwest Papua Province, Indonesia · The Directorate of Scientific Collection Management, National Research and Innovation Agency (BRIN), Republic of Indonesia, Gedung Kehati, KST Soekarno BRIN, Jl. Raya Jakarta - Bogor KM 46, Cibinong, Kabupaten Bogor, Jawa Barat, 16911, Indonesia.+5
New Guinea is the world's richest island flora (~2,856 orchid species), yet most species are represented by only a handful of photographs, far fewer than direct species-level classification requires. Methods for fine-grained identification in such species-rich, data-poor floras are needed, and it remains unclear which backbone architecture and pretraining strategy best support them. We built a two-stage system that first predicts the genus of a query photograph, then retrieves visually similar reference images of candidate species using FAISS. We compared four pretrained backbones -- two Vision Transformers (ViTs; DINOv2, BioCLIP 2) and two CNNs (ConvNeXt V2-L, EfficientNetV2-L) -- fine-tuned under an identical protocol on a fixed, species-stratified partition of 16,701 photographs spanning 120 genera and 1,350 species, assessing accuracy, calibration, error structure, species retrieval, and open-set detection of novel genera. DINOv2 attained the best genus performance (macro top-1 66.9%, 95% CI 63.7-70.6; global top-1 88.9%); both ViTs outranked both CNNs, and general-purpose self-supervised pretraining (DINOv2) outperformed domain-matched biological pretraining (BioCLIP 2) by 7.1 points of macro top-1. Errors concentrated on two abundant genera acting as error attractors. DINOv2 embeddings achieved species Recall@5 of 86.6% and genus Recall@5 of 98.7%; temperature scaling reduced every backbone's Expected Calibration Error to about 0.03; and a distance-based open-set gate flagged unseen genera (mean AUROC 0.958). A self-supervised Vision-Transformer backbone combined with embedding retrieval is an effective, deployable strategy for fine-grained identification in species-rich, data-poor floras. The system is released as an open web application (the New Guinea Orchid Identifier), offering a practical template for other hyperdiverse, under-documented taxa.