Autonomous AI Bird Feeder for Backyard Biodiversity Monitoring
This enables citizen-science-grade biodiversity logging at home, addressing a domain-specific need for urban garden monitoring.
The paper tackles the problem of autonomous backyard bird monitoring by developing a low-cost, on-premise system that uses motion-triggered cameras and deep learning models, achieving about 88% top-1 accuracy on held-out species for practical field use.
This paper presents a low cost, on premise system for autonomous backyard bird monitoring in Belgian urban gardens. A motion triggered IP camera uploads short clips via FTP to a local server, where frames are sampled and birds are localized with Detectron2; cropped regions are then classified by an EfficientNet-B3 model fine tuned on a 40-species Belgian subset derived from a larger Kaggle corpus. All processing runs on commodity hardware without a discrete GPU, preserving privacy and avoiding cloud fees. The physical feeder uses small entry ports (30 mm) to exclude pigeons and reduce nuisance triggers. Detector-guided cropping improves classification accuracy over raw-frame classification. The classifier attains high validation performance on the curated subset (about 99.5 percent) and delivers practical field accuracy (top-1 about 88 percent) on held-out species, demonstrating feasibility for citizen-science-grade biodiversity logging at home.