Geotag Tree (One Acre Fund)

This project aims to support One Acre Fund in scaling its tree census and monitoring efforts through a low resource computer vision assisted system for identifying and tracking young trees in dense, non grid planted agroforestry plots. Enumerators currently collect geotagged images, record species, and manually enter planting year, but GPS imprecision, closely spaced trees, blurry images of seedlings, and lack of consistent scale make reliable identification and diameter estimation difficult. The project explores whether computer vision models can reach confidence levels high enough to be useful at scale, potentially leveraging visual cues such as painted markings, QR tags, or low cost physical identifiers introduced after a survival threshold, given that tagging all seedlings upfront is impractical. The work will evaluate tradeoffs between accuracy, cost, and time efficiency across sensing approaches such as standard smartphone photography, QR based scale references, and emerging mobile LiDAR on select consumer devices. With hundreds of trees per acre, multiple species per country, and survival rates of 40 to 60 percent, the project focuses on identifying field ready tools and workflows that meaningfully improve data quality while remaining feasible in low resource, high volume environments.