arXiv:2609.09417v1 Announce Type: new
Abstract: Vision-language models (VLMs) show promise for agricultural classification, but zero-shot performance on disease, pest, damage, quality, and species id...
By Earl Ranario, Jared Smith, Lars Lundqvist, Urmil Jatin Chandarana
arXiv:2606.20223v2 Announce Type: replace
Abstract: Camera-trap monitoring in African tropical forests increasingly extends beyond closed-canopy interiors to riverbanks, clearings, and park edges. Am...
By Hugo Magaldi, Theau d'Audiffret, Etienne Francois Akomo-Okoue, Bala Amarasekaran, Naomi Anderson, Claire Auger, Noemie Cappelle, Daniel Cornelis, Raphael Cornette, Tobias Deschner, Gabriel Dubus, Davy Fonteyn, Rosa M. Garriga, Jennifer Hatlauf, Innocent Kasekendi, Raymond Katumba, Aram Kazandjian, Alfred Ngomanda, Stephan Ntie, Simone Pika, Xavier Rufray, Harold Rugonge, John Justice Tibesigwa, Peter van Lunteren, Hadrien Vanthomme, Joeri A. Zwerts, Sabrina Krief
FieldGuideRAG is a training‑free GraphRAG approach that uses a frozen vision‑language model to recognize fine‑grained species by comparing a query image to photographs stored on each species node in a knowledge graph. The graph links species to relatives, geographic range, and ecological facts, and the system fuses verification scores with range edges to answer organism‑related questions. On datasets such as CUB‑200 and iNaturalist, FieldGuideRAG’s use of node photographs improves verification over species names, surpasses a SigLIP retriever by up to 16 points, and achieves up to 89.5% accuracy on ecological questions.
By Jiacheng Ding, Yanliang Qi, Cong Guo, Peihua Mai
arXiv:2610.32573v1 Announce Type: new
Abstract: Camera-trap species classification is dominated by large models from well-resourced organisations, which often lack the species-level distinctions regi...
By Paul Fergus
arXiv:2606. 10940v1 Announce Type: cross Abstract: Camera traps have become a cornerstone of biodiversity monitoring, but the artificial intelligence that turns vast quantities of images into usable ecological data is often locked behind commercial platforms or trained on fauna that does not match that of the British Isles.
By Paul Fergus, Philip Stephens, Russell A. Hill, Lee Oliver, Katie Appleby, Sarah Beatham, Naomi Davies Walsh, Stuart Nixon, Naomi Matthews, Chris Sutherland, Kelly Hitchcock
Vision-language models (VLMs) are often reported to outperform task-specific vision backbones for unmanned aerial vehicle (UAV) power-line defect assessment. We test that claim on ElecVQA-Bench, a 56,...