With a limited amount of arable land, increasing demand for food induced by growth in population can only be meet with more effective crop production and more resistant plants. Since crop plants are exposed to many different stress factors, it is relevant to investigate those factors as well as their behavior and reactions. One of the most severe stress factors are diseases, resulting in a high loss of cultivated plants. Our main objective is the forecasting of the spread of disease symptons on barley plants using a Cycle-Consistent Generative Adversarial Network. Our contributions are: (1) we provide a daily forecast for one week to advance research for better planning of plant protection measures, and (2) in contrast to most approaches which use only RGB images, we learn a model with hyperspectral images, providing an information-rich result. In our experiments, we analyze healthy barley leaves and leaves which were inoculated by powdery mildew. Images of the leaves were acquired daily with a hyperspectral microscope, from day 3 to day 14 after inoculation. We provide two methods for evaluating the predicted time series with respect to the reference time series

@inproceedings{foerster2019a,
    abstract = {With a limited amount of arable land, increasing demand for
food induced by growth in population can only be meet with
more effective crop production and more resistant plants.
Since crop plants are exposed to many different stress factors, it is relevant to investigate those factors as well as their
behavior and reactions. One of the most severe stress factors
are diseases, resulting in a high loss of cultivated plants. Our
main objective is the forecasting of the spread of disease
symptons on barley plants using a Cycle-Consistent Generative Adversarial Network. Our contributions are: (1) we
provide a daily forecast for one week to advance research
for better planning of plant protection measures, and (2) in
contrast to most approaches which use only RGB images,
we learn a model with hyperspectral images, providing an
information-rich result. In our experiments, we analyze
healthy barley leaves and leaves which were inoculated by
powdery mildew. Images of the leaves were acquired daily
with a hyperspectral microscope, from day 3 to day 14 after inoculation. We provide two methods for evaluating the
predicted time series with respect to the reference time series},
    author = {Förster, A. and Behley, J. and Behmann, J. and Roscher, R.},
    booktitle = {International Geoscience and Remote Sensing Symposium},
    date-added = {2019-05-26 21:01:17 +0200},
    date-modified = {2019-05-26 21:07:38 +0200},
    title = {Hyperspectral plant disease forecasting using generative adversarial networks},
    year = {2019}
}

Understanding the criteria that bicyclists apply when they choose their routes is crucial for planning new bicycle paths or recommending routes to bicyclists. This is becoming more and more important as city councils are becoming increasingly aware of limitations of the transport infrastructure and problems related to automobile traffic. Since different groups of cyclists have different preferences, however, searching for a single set of criteria is prone to failure. Therefore, in this paper, we present a new approach to classify trajectories recorded and shared by bicyclists into different groups and, for each group, to identify favored and unfavored road types. Based on these results we show how to assign weights to the edges of a graph representing the road network such that minimumweight paths in the graph, which can be computed with standard shortest-path algorithms, correspond to adequate routes. Our method combines known algorithms for machine learning and the analysis of trajectories in an innovative way and, thereby, constitutes a new comprehensive solution for the problem of deriving routing preferences from initially unclassified trajectories. An important property of our method is that it yields reasonable results even if the given set of trajectories is sparse in the sense that it does not cover all segments of the cycle network.

doi:10.5194/isprs-annals-IV-4-W7-107-2018

@inproceedings{OehrleinEtAl2018,
    abstract = {Understanding the criteria that bicyclists apply when they choose their routes is crucial for planning new bicycle paths or recommending routes to bicyclists. This is becoming more and more important as city councils are becoming increasingly aware of limitations of the transport infrastructure and problems related to automobile traffic. Since different groups of cyclists have different preferences, however, searching for a single set of criteria is prone to failure. Therefore, in this paper, we present a new approach to classify trajectories recorded and shared by bicyclists into different groups and, for each group, to identify favored and unfavored road types. Based on these results we show how to assign weights to the edges of a graph representing the road network such that minimumweight paths in the graph, which can be computed with standard shortest-path algorithms, correspond to adequate routes. Our method combines known algorithms for machine learning and the analysis of trajectories in an innovative way and, thereby, constitutes a new comprehensive solution for the problem of deriving routing preferences from initially unclassified trajectories. An important property of our method is that it yields reasonable results even if the given set of trajectories is sparse in the sense that it does not cover all segments of the cycle network.},
    author = {Oehrlein, J. and Förster, A. and Schunck, D. and Dehbi, Y. and Roscher, R. and Haunert, J.-H.},
    booktitle = {Proc. 3rd International Conference on Smart Data and Smart Cities},
    doi = {10.5194/isprs-annals-IV-4-W7-107-2018},
    pages = {107--114},
    series = {ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences},
    title = {Inferring Routing Preferences of Bicyclists from Sparse Sets of Trajectories},
    volume = {IV-4/W7},
    year = {2018}
}