Deepweed: Automated Individual Segmentation and Tracking for Duckweed Lineage Analysis
Abstract
Motivation: Aquatic duckweeds, including Wolffia australiana, are the smallest and fastest growing flowering plants on the planet, with numerous emerging biotechnological applications. Duckweed populations grow through rapid clonal budding, but existing phenotypic characteriza-tion and image analysis tools typically operate only at the population level, do not track individual plants, or reconstruct lineage structure. This limits developmental, physiological, and environ-mental studies that require lineage-resolved growth dynamics. Deepweed, our computational workflow for automated individual segmentation, addresses this gap with temporal tracking, and clonal lineage reconstruction from time-lapse imaging of duckweed. Results: Deepweed processes time-lapse duckweed imaging datasets using deep-learning–based instance segmentation and multi-object tracking to assign persistent identities to individual fronds and infer mother–daughter relationships. The workflow outputs lineage graphs, individual-level morphological metrics, and population-level growth summaries, supported by lineage-annotated time-lapse visualizations. Applied to dense Wolffia populations, in both freely growing or controlled microfluidic environments, our framework can track hundreds of Wolffia plants sim-ultaneously, resolves overlapping individuals, and reconstructs multi-generation lineage structure, enabling analyses that are inaccessible with population-level phenotyping methods Availability: Deepweed is implemented in Python using PyTorch, scikit-image, OpenCV, and btrack. Source code, trained models, example datasets, and documentation are freely available at https://github.com/SamOliveiraLab/Deepweed. The Deepweed code is archived at https://doi.org/10.5281/zenodo.18844283 and the datasets are archived at https://doi.org/10.5281/zenodo.18843918.