@article{koerschens2025plantcapnet,
    type = {article},
    key = {koerschens2025plantcapnet},
    author = {Matthias Körschens and Solveig Franziska Bucher and Paul Bodesheim and Joachim Denzler and Christine Römermann},
    title = {PlantCAPNet: A Deep Learning System For Image-based Plant Cover and Phenology Analysis},
    journal = {Ecological Informatics},
    volume = {91},
    pages = {103413},
    year = {2025},
    issn = {1574-9541},
    doi = {10.1016/j.ecoinf.2025.103413},
    url = {https://www.sciencedirect.com/science/article/pii/S1574954125004224},
    abstract = {Plant community data, like the species composition of the community and the phenology of the occurring species, are paramount for environmental research. Such data can be used to detect species responses to environmental changes, but the collection is very laborious, slow, and prone to human error. These detriments can be counteracted with automatic camera systems in combination with machine learning approaches that are able to extract the vegetation data from collected images in a consistent and fast manner. We introduce PlantCAPNet, an application to automate the analysis of herbaceous plant communities from images by extracting plant cover and phenology, addressing the tedious and biased nature of manual field collection. The system has an easy-to-use web interface with a single image prediction tool, a batch prediction function for image series, and a training interface for users to build novel models. We offer PlantCAPNet with two operational modes: a ’cover-trained’ mode for predicting cover and phenology using user-provided labeled data, and a ’zero-shot’ mode capable of predicting cover using only web-sourced data, thus lowering the barrier for entry. Our evaluations show that PlantCAPNet performs comparably or better than independent human experts in estimating plant cover. The zero-shot method reflects the reference estimates with a correlation of 0.625, and the cover-trained method with one of 0.790 compared to a correlation of 0.620 from independent experts. Moreover, we show that our system performs reliably for dataset with few species, and the cover prediction is also reliable for the most abundant species in datasets with many species, while the phenology prediction is dependent on the amount of training data. In total, our system offers higher consistency than human experts, and enables the extraction of high-temporal-resolution ecological data, facilitating novel environmental research.},
    groups = {biodiversity,pheneye},
}