Using Deep Learning to Automate Orangutan Nest Detections on Aerial Images Collected With Drones

Wich, S ORCID: 0000-0003-3954-5174, Ancrenaz, M, Goossens, B ORCID: 0000-0003-2360-4643, Hennekam, M, Milne, S, Burslem, D ORCID: 0000-0001-6033-0990, Knott, C ORCID: 0000-0002-1940-7199, Martin, J ORCID: 0000-0001-7726-6809 and Fergus, P ORCID: 0000-0002-7070-4447 (2025) Using Deep Learning to Automate Orangutan Nest Detections on Aerial Images Collected With Drones. American Journal of Primatology, 87 (12). pp. 1-8. ISSN 0275-2565

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Abstract

Traditional orangutan distribution and density monitoring requires costly line transect methods on the ground to detect their nests. Recently researchers have started to use unoccupied aerial vehicles, hereafter referred to as drones, to collect such data faster. However, manually inspecting the images acquired by the drone is time-consuming and hence costly. This study explored a deep learning method for the automated detection of orangutan nests in drone-captured aerial images, which can significantly improve the efficiency of orangutan monitoring efforts. The YOLO v10 model was trained using 868 images containing 1568 annotated orangutan nests collected from sites in Sabah, Malaysia, and Sumatra, Indonesia. Images were captured using multirotor and fixed-wing drones at varying altitudes. The model was trained using a transfer learning approach and achieved a mean Average Precision (mAP) of 0.831. The model was subsequently tested on two independent data sets with results showing a precision of 0.98 and recall of 0.88 for a multirotor drone and precision of 0.98 and a recall of 0.71 for a fixed-wing drone which has the benefit of being able to have longer duration flights. The high precision values indicate the model's accuracy in identifying true nest locations, while the recall values demonstrate its ability to detect a significant portion of the nests present in the images. The study highlights how using drones for data collection can reduce survey times compared to ground surveys, and the automation of nest detection further enhances the efficiency of drone surveys. However, the model's recall, especially for fixed-wing drone data, could be improved to ensure accurate population trend analyses. Further research should focus on expanding training data sets and refining models to account for different camera systems and environmental conditions.

Item Type:	Article
Uncontrolled Keywords:	Animals; Nesting Behavior; Indonesia; Malaysia; Pongo; Deep Learning; Unmanned Aerial Devices; Indonesia; Malaysia; great apes; line transects; monitoring; Animals; Deep Learning; Indonesia; Malaysia; Nesting Behavior; Unmanned Aerial Devices; Pongo; 3109 Zoology; 31 Biological Sciences; Networking and Information Technology R&D (NITRD); Machine Learning and Artificial Intelligence; Animals; Deep Learning; Indonesia; Malaysia; Nesting Behavior; Unmanned Aerial Devices; Pongo; 0608 Zoology; 1601 Anthropology; Behavioral Science & Comparative Psychology; 3109 Zoology
Subjects:	G Geography. Anthropology. Recreation > GE Environmental Sciences T Technology > T Technology (General)
Divisions:	Biological and Environmental Sciences (from Sep 19) Computer Science and Mathematics
Publisher:	Wiley
Date of acceptance:	27 September 2025
Date of first compliant Open Access:	22 December 2025
Date Deposited:	22 Dec 2025 14:46
Last Modified:	22 Dec 2025 14:46
DOI or ID number:	10.1002/ajp.70100
URI:	https://researchonline.ljmu.ac.uk/id/eprint/27762

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