# AI-Driven Real-Time Monitoring of Ground-Nesting Birds: A Case Study on Curlew Detection Using YOLOv10

arXiv: https://arxiv.org/abs/2411.15263 · HTML: https://mlsanity.com/paper/2411.15263/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 31 references that are in the map.
- First posted on arXiv 22 Nov 2024.

## Builds on (most cited first, 9 of 9)

- [YOLOv10: Real-Time End-to-End Object Detection](https://mlsanity.com/paper/2405.14458/) (2024)
- [Large Scale Fine-Grained Categorization and Domain-Specific Transfer Learning](https://mlsanity.com/paper/1806.06193/) (2018)
- [Efficient Pipeline for Camera Trap Image Review](https://mlsanity.com/paper/1907.06772/) (2019)
- [Pytorch-Wildlife: A Collaborative Deep Learning Framework for Conservation](https://mlsanity.com/paper/2405.12930/) (2024)
- [Targeted transfer learning to improve performance in small medical physics datasets](https://mlsanity.com/paper/1912.06761/) (2019)
- [Harnessing Artificial Intelligence for Wildlife Conservation](https://mlsanity.com/paper/2409.10523/) (2024)
- [Model Generalization in Deep Learning Applications for Land Cover Mapping](https://mlsanity.com/paper/2008.10351/) (2020)
- [Removing Human Bottlenecks in Bird Classification Using Camera Trap Images and Deep Learning](https://mlsanity.com/paper/2305.02097/) (2023)
- [Reviving the Context: Camera Trap Species Classification as Link Prediction on Multimodal Knowledge Graphs](https://mlsanity.com/paper/2401.00608/) (2023)

## Built on by (most cited first, 1 of 1)

- [Democratising Camera Trap AI: An Open-Source Model for Detecting UK Mammals](https://mlsanity.com/paper/2606.10940/) (2026)

## Abstract (arXiv)

Effective monitoring of wildlife is critical for assessing biodiversity and ecosystem health, as declines in key species often signal significant environmental changes. Birds, particularly ground-nesting species, serve as important ecological indicators due to their sensitivity to environmental pressures. Camera traps have become indispensable tools for monitoring nesting bird populations, enabling data collection across diverse habitats. However, the manual processing and analysis of such data are resource-intensive, often delaying the delivery of actionable conservation insights. This study presents an AI-driven approach for real-time species detection, focusing on the curlew (Numenius arquata), a ground-nesting bird experiencing significant population declines. A custom-trained YOLOv10 model was developed to detect and classify curlews and their chicks using 3/4G-enabled cameras linked to the Conservation AI platform. The system processes camera trap data in real-time, significantly enhancing monitoring efficiency. Across 11 nesting sites in Wales, the model achieved high performance, with a sensitivity of 90.56%, specificity of 100%, and F1-score of 95.05% for curlew detections, and a sensitivity of 92.35%, specificity of 100%, and F1-score of 96.03% for curlew chick detections. These results demonstrate the capability of AI-driven monitoring systems to deliver accurate, timely data for biodiversity assessments, facilitating early conservation interventions and advancing the use of technology in ecological research.