Autonomous AI Bird Feeder for Backyard Biodiversity Monitoring

• URL: http://arxiv.org/abs/2508.09398v1
• Date: Wed, 13 Aug 2025 00:27:22 GMT
• Title: Autonomous AI Bird Feeder for Backyard Biodiversity Monitoring
• Authors: El Mustapha Mansouri,
• Abstract summary: This paper presents a low cost, on premise system for autonomous backyard bird monitoring in Belgian urban gardens.<n>A motion triggered IP camera uploads short clips via FTP to a local server, where frames are sampled and birds are localized with Detectron2.<n> cropped regions are then classified by an EfficientNet-B3 model fine tuned on a 40-species Belgian subset derived from a larger Kaggle corpus.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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