arXiv Machine Learning By Leutrim Uka, Severino Pinto, Gundula Hoffmann, Marina M. -C. H\"ohne

When Multi-Sensor Fusion Fails to Generalize: Cattle Posture Classification Under Animal-Level and Temporal Distribution Shift

Read the original on arXiv Machine Learning →

arXiv:2606. 24986v1 Announce Type: new Abstract: Automated cattle posture-classification systems frequently report near-perfect accuracy, yet their robustness under realistic deployment conditions remains largely unknown.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

arXiv AI
Jun 10

Democratising Camera Trap AI: An Open-Source Model for Detecting UK Mammals

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
Hugging Face Trending Papers
Jun 9

Democratising Camera Trap AI: An Open-Source Model for Detecting UK Mammals

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 Machine Learning
Aug 12

EweAcT: Ewe behaviour aligned to accelerometer data for activity monitoring in extensive grazing systems

arXiv:2608. 09943v1 Announce Type: cross Abstract: Monitoring livestock behaviour under extensive conditions would provide valuable insights to assess animal adaption to environmental perturbations in agroecological systems (e.

By Lucile Riaboff (GenPhySE, INRAE), Ny Aina Andriamampandry (GenPhySE, GenPhySE), Jean-Fran\c{c}ois Bompa (GenPhySE, GenPhySE), Mathias Aletru (GenPhySE, GenPhySE), Christian Durand (UEF), S\'ebastien Douls (UEF), Ga\"etan Bonnafe (UEF), Morgane Costes-Thir\'e (GenPhySE, GenPhySE), Guillaume Delosi\`eres (GenPhySE, GenPhySE), Jean- Marc Mongrelet (GenPhySE, GenPhySE), Enzo Niro (GenPhySE, GenPhySE), N\'emuel Tadi (GenPhySE, GenPhySE), S\'everine Deretz (DEPT GA, UEF, INRAE), Sara Parisot (UEF), Margot Lamarque (UEF), Dominique Hazard (GenPhySE), Emilie Cobo (GenPhySE)
arXiv AI
Jun 16

Advanced Machine Learning and Deep Learning Techniques for Enhanced Cattle Identification and Detection: A Comprehensive Review

arXiv:2606. 15655v1 Announce Type: new Abstract: The need for effective cattle identification technology is now more acutely felt than ever in maintaining biosecurity, food safety, and supply chain efficacy in livestock management.

By Fayazunnesa Chowdhury, Syed Md. Galib, Md Nasim Adnan, Md. Moradul Siddique, Md Robiul Karim, K M Tanvir Anjum
arXiv AI
Jun 12

GetNetUPAM: Ecologically Informed Nested Cross-Validation and Noise-Robust Attention for Marine Bioacoustic Monitoring

arXiv:2509. 04682v2 Announce Type: replace-cross Abstract: Deploying reliable bioacoustic monitoring systems requires models that generalize under high-noise, low-SNR conditions and evaluation protocols that expose deployment-relevant failure modes, gaps largely unaddressed in current UPAM practice.

By Nicholas R. Rasmussen, Rodrigue Rizk, Longwei Wang, KC Santosh