Closing the Gap in Arboreal Wildlife Monitoring

TropiCam-AI: New AI Model Monitors Tree-Dwelling Species

Researchers launch a deep-learning tool with 95% accuracy to track mammals and birds in Neotropical forest canopies.

By Kronos Digital News Desk··1 min read
A scientific camera trap mounted on a mossy tree branch high in a dense tropical forest canopy, used for monitoring arboreal wildlife.

A scientific camera trap mounted on a mossy tree branch high in a dense tropical forest canopy, used for monitoring arboreal wildlife.

Photo: Kronos Digital News

Researchers have developed TropiCam-AI, a deep-learning model designed to identify wildlife living in the forest canopy [1]. The tool focuses on Neotropical forests, where it can identify 84 different taxa of mammals and birds [1][3]. Most existing wildlife AI models focus on ground-level data, often overlooking species that live primarily in trees [1][2].

The new system achieves a high identification accuracy of 95% using data collected from camera traps [2]. By automating the analysis of these images, TropiCam-AI aims to fill a critical gap in biodiversity monitoring [1][2]. This project represents a significant advancement for conservation efforts in complex tropical environments [3].

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Kronos Digital News Desk covers closing the gap in arboreal wildlife monitoring and editorial analysis for Kronos Digital News.