DeepForestVision: Automated wildlife identification for camera traps of African tropical forests

Abstract

Tropical forests are rich in biodiversity but face the rapid loss of their wildlife due to increasing anthropogenic pressure, underscoring the urgent need for effective monitoring. Remote-sensing tools such as camera traps offer faster, less invasive alternatives to human observations. These technologies can provide complementary insights into elusive species, especially in habitats that are difficult to access through direct observation. However, analysing the large volumes of data they produce is labour-intensive, often leaving datasets underutilised due to limited human resources. Deep learning algorithms can automate aspects of data analysis, but their value to research and conservation efforts depends on their ability to reliably identify target species and be easily deployed in field conditions. To improve wildlife monitoring in African forests using camera trap data, we develop DeepForestVision, the first deep learning algorithm tailored to these challenging habitats that can be used in the field to process both photographs and videos. DeepForestVision was trained on an unprecedented dataset of 2,775,671 photographs and 221,982 videos gathered from camera traps from more than 63 research sites across 11 African countries. It identifies 33 non-human vertebrate taxa, including 31 mammal taxa, from the most common to the most threatened ones observed on ground-level camera traps, as well as humans, vehicles and blank photographs or videos. Classification tests demonstrate that DeepForestVision achieves an accuracy of 87.7% on the video test set with 23 taxa. It outperforms the three existing species identification algorithms applicable to these environments: Zamba by 13.1%, Mbaza by 45.0% and SpeciesNet by 37.7%. We provide the model weights for researchers and developers and offer DeepForestVision through a free offline interface. The interface is designed to function in the field in a low-resource setting and requires no programming expertise. Solution: DeepForestVision is a reliable field tool for monitoring species observed on camera trap photos and videos in African tropical forests. Used by research, conservation, private or political actors, it can guide conservation strategies inside and outside of protected areas, and thus contribute to reducing the loss of biodiversity in African tropical forests.