Autonomous AI Bird Feeder for Backyard Biodiversity Monitoring

TL;DR

This paper introduces a low-cost, autonomous bird monitoring system for urban gardens that uses computer vision and machine learning to identify bird species in real-time without relying on cloud services.

Contribution

It presents a novel integrated system combining motion-triggered cameras, detector-guided cropping, and efficient classification models for privacy-preserving backyard biodiversity monitoring.

Findings

High classifier accuracy (~99.5%) on curated dataset

Practical field accuracy (~88%) on unseen species

System operates on commodity hardware without cloud dependency

Abstract

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.