@inproceedings{Koerschens23:CVPPA23,
    type = {inproceedings},
    key = {Koerschens23:CVPPA23},
    title = {Unified Automatic Plant Cover and Phenology Prediction},
    author = {Matthias Körschens and Solveig Franziska Bucher and Christine Römermann and Joachim Denzler},
    booktitle = {ICCV Workshop on Computer Vision in Plant Phenotyping and Agriculture (CVPPA)},
    year = {2023},
    month = {October},
    note = {},
    abstract = {The composition and phenology of plant communities are paramount indicators for environmental changes, especially climate change, and are, due to this, subject to many ecological studies. While species composition and phenology are usually monitored by ecologists directly in the field, this process is slow, laborious, and prone to human error. In contrast, automated camera systems with intelligent image analysis methods can provide fast analyses with a high temporal resolution and therefore are highly advantageous for ecological research. Nowadays, methods already exist that can analyze the plant community composition from images, and others that investigate the phenology of plants. However, there are no automatic approaches that analyze the plant community composition together with the phenology of the same community, which is why we aim to close this gap by combining an existing plant cover prediction method based on convolutional neural networks with a novel phenology prediction module. The module builds on the species- and pixel-wise occurrence probabilities generated during the plant cover prediction process, and by that, significantly improves the quality of phenology predictions compared to isolated training of plant cover and phenology. We evaluate our approach by comparing the time trends of the observed and predicted phenology values on the InsectArmageddon dataset comprising cover and phenology data of eight herbaceous plant species. We find that our method significantly outperforms two dataset-statistics-based prediction baselines as well as a naive baseline that does not integrate any information from the plant cover prediction module.},
    groups = {biodiversity},
}