New AI model identifies 40 wildlife species with 97.3 per cent accuracy from camera-trap images

Camera-trap images seamlessly classified by the AI species recognition model. Pics/Wildlife Conservation Trust

Imagine opening a camera trap and finding thousands of wildlife photographs waiting to be sorted. An open-source artificial intelligence (AI) model can now take over much of that work. Trained on more than 1.1 million camera-trap images collected from over 10,000 locations across the dry and moist deciduous forests of Central India, the model identifies 40 classes of wild animals with 97.3 per cent accuracy, offering wildlife researchers and forest department officials a faster way to analyse data generated during wildlife surveys.

Manual challenge

Wildlife researchers and forest department officials using camera traps often collect thousands, and sometimes lakhs, of images. Sorting these photographs and identifying species manually is tedious and time-consuming.

Regional model

Wildlife Conservation Trust (WCT), in collaboration with Aadax Data Science, has developed an automated species recognition model for the Central Indian landscape. Using machine learning and computer vision, it automates species classification, addressing a major bottleneck in wildlife research.

Data advantage

WCT's camera-trap database, built over years of systematic surveys, formed the foundation of the model. It ensures consistent species identification, reduces observer bias, and delivers standardised, reproducible results. It also allows researchers to expand studies without a proportional increase in labour, enabling greater focus on fieldwork, ecological analysis and conservation.

Official Speak

Dr Anish Andheria, president, WCT
"We are launching an open-source species recognition programme for Central India, so that camera-trap data can transform into rapid, usable science. Built by WCT with Aadax Data Science, this easy-to-use programme will cut time, cost, and observer bias. Now students, researchers, and forest officers can carry out their respective data analysis faster, with wider reach and confidence - fuelling better species and habitat conservation for everyone."

Aditya Joshi, head, WCT's Conservation Research - Central India Programme
"Species recognition models trained on global datasets already exist, but fine-tuning them with region-specific images boosts their accuracy, since these training datasets capture local variations in animal appearance and habitat backgrounds, resulting in more accurate species identification from camera trap data collected in that landscape."

Anuj Alukathra, assistant programme manager, WCT
"This model doesn't just save thousands of hours of manual labour, but it also brings standardised, bulletproof scientific credibility to wildlife monitoring across Central India. More importantly, it's open source and runs on your machine offline and requires zero coding skills."