A low-cost Raspberry Pi machine learning system continuously detecting and logging bird species in urban Brooklyn — testing whether automated systems can scale urban biodiversity monitoring.
Can a low-cost, automated machine learning detection system produce species diversity data statistically consistent with Cornell Lab of Ornithology's Project FeederWatch citizen science dataset — and could such systems scale urban biodiversity monitoring beyond what citizen science alone can cover?
This project sits at the intersection of machine learning, field ecology, and urban science. The immediate goal is a single-site deployment that generates a reliable, continuous dataset of automated bird detections through a full season.
The longer-term goal is comparative: to test whether that automated dataset is statistically consistent with Project FeederWatch's citizen-science observations for the same region, and to use that comparison to evaluate whether low-cost automated systems could extend the reach of urban biodiversity monitoring beyond what volunteer observers alone can cover.
The detection system runs continuously on a Raspberry Pi paired with a fixed camera module, classifying visiting birds in real time and logging each detection with species, confidence score, and timestamp.
| AMGO | American Goldfinch |
| BCCH | Black-capped Chickadee |
| BLJA | Blue Jay |
| DEJU | Dark-eyed Junco |
| DOWO | Downy Woodpecker |
| HOFI | House Finch |
| HOSP | House Sparrow |
| MODO | Mourning Dove |
| NOCA | Northern Cardinal |
| TUTI | Tufted Titmouse |
Detection logs, R analysis, and comparative Project FeederWatch visualizations will be published here as the dataset grows. Data is updated weekly. The full raw dataset will be made available for download once sufficient records have been collected.
I'm a rising junior at Packer Collegiate Institute in Brooklyn, NY, interested in the intersection of data science, machine learning, and environmental science — specifically how computational tools can help us understand and protect urban ecosystems.
NYC FeederWatch AI began in February 2026 as an independent research project. The core question driving it: can a low-cost automated system produce biodiversity data as reliable as human citizen science? The answer has implications for how conservation organizations monitor urban wildlife at scale.
I welcome correspondence from researchers, conservationists, and anyone interested in automated biodiversity monitoring.