  UAV-based image retrieval in modern agriculture enables gathering large
amounts of spatially referenced crop image data. In large-scale experiments,
however, UAV images suffer from containing a multitudinous amount of crops in a
complex canopy architecture. Especially for the observation of temporal
effects, this complicates the recognition of individual plants over several
images and the extraction of relevant information tremendously. In this work,
we present a hands-on workflow for the automatized temporal and spatial
identification and individualization of crop images from UAVs abbreviated as
"cataloging" based on comprehensible computer vision methods. We evaluate the
workflow on two real-world datasets. One dataset is recorded for observation of
Cercospora leaf spot - a fungal disease - in sugar beet over an entire growing
cycle. The other one deals with harvest prediction of cauliflower plants. The
plant catalog is utilized for the extraction of single plant images seen over
multiple time points. This gathers large-scale spatio-temporal image dataset
that in turn can be applied to train further machine learning models including
various data layers. The presented approach improves analysis and
interpretation of UAV data in agriculture significantly. By validation with
some reference data, our method shows an accuracy that is similar to more
complex deep learning-based recognition techniques. Our workflow is able to
automatize plant cataloging and training image extraction, especially for large
datasets.
