@inproceedings{koerschens2020towards,
    type = {inproceedings},
    key = {koerschens2020towards},
    title = {Towards Confirmable Automated Plant Cover Determination},
    author = {Matthias Körschens and Paul Bodesheim and Christine Römermann and Solveig Franziska Bucher and Josephine Ulrich and Joachim Denzler},
    booktitle = {ECCV Workshop on Computer Vision Problems in Plant Phenotyping (CVPPP)},
    year = {2020},
    month = {August},
    abstract = {Changes in plant community composition reflect environmental changes like in land-use and climate. While we have the means to record the changes in composition automatically nowadays, we still lack methods to analyze the generated data masses automatically. We propose a novel approach based on convolutional neural networks for analyzing the plant community composition while making the results explainable for the user. To realize this, our approach generates a semantic segmentation map while predicting the cover percentages of the plants in the community. The segmentation map is learned in a weakly supervised way only based on plant cover data and therefore does not require dedicated segmentation annotations. Our approach achieves a mean absolute error of 5.3% for plant cover prediction on our introduced dataset with 9 herbaceous plant species in an imbalanced distribution, and generates segmentation maps, where the location of the most prevalent plants in the dataset is correctly indicated in many images.},
    doi = {10.1007/978-3-030-65414-6_22},
    groups = {biodiversity},
    url = {https://doi.org/10.1007/978-3-030-65414-6_22},
}