arXiv:2609. 11916v1 Announce Type: new Abstract: Camera traps often run in the field on edge hardware with limited or no connectivity, making small, locally-deployable vision-language models (VLMs) -- not frontier-scale ones -- the practically relevant class to evaluate for species identification.
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...
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...
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.
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...
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.
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,...
The paper evaluates the claim that vision‑language models (VLMs) outperform task‑specific vision backbones for UAV power‑line defect assessment using the ElecVQA‑Bench benchmark. Across various evaluation settings—partitioning, item sets, label spaces, replication, resolution, and side information—the performance gap between VLMs and traditional backbones is minimal or even reversed when controlling for resolution and token budget. The study concludes that VLM superiority is not universally supported and emphasizes the importance of rigorous benchmark audits.
The paper presents a configurable multi‑stage vision pipeline for Farmer.Chat, a farm advisory service that processes farmer‑submitted crop photos. It splits the task into a quality gate, a crop detector, and a disease/pest detector, offering two routes: a single fine‑tuned vision‑language model and a set of small specialist models. The new pipeline improves crop accuracy to 95.41% and provides a fast MobileNetV3 quality gate, while retaining the ability to request better photos and answer all queries in one call.
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. In an attempt to remove barriers and increase uptake, we release an open-source object detection model for 31 classes, 28 common UK mammal and bird species, plus utility classes for humans, calibration poles, and vehicles, drawn from a curated dataset of 48,165 labelled instances assembled from multiple sites over a decade of operational deployment through Conservation AI and its successor, Trap Tracker.
arXiv:2609.22897v1 Announce Type: cross Abstract: Large vision-language models (VLMs) enable recognition beyond a fixed class set, but their computational demands prevent them from running on many ed...
arXiv:2608.30789v1 Announce Type: new Abstract: Supervised deep learning methods enable the rapid processing of ecological image data, but depend on a costly annotation process. Consequently, trainin...
WoodBR-Cross is a new public dataset of 4,875 wood surface images grouped into 38 taxonomic classes and linked to 303 distinct trees, with a species-level subset of 3,406 images across 20 classes and 214 trees. The benchmark introduces three tasks that compare duplicate-aware image partitions, species subset partitions, and individual-tree-disjoint partitions, ensuring no tree appears in both training and test sets within a fold. Experiments with ResNet‑50, ConvNeXt‑Tiny, and ViT‑B/16 show that ConvNeXt‑Tiny and frozen DINOv2 features achieve macro‑F1 scores of 0.8621 and 0.9000, respectively, on the tree‑level disjoint split, providing realistic estimates of performance on unseen trees.