  Crop classification via deep learning on ground imagery can deliver timely
and accurate crop-specific information to various stakeholders. Dedicated
ground-based image acquisition exercises can help to collect data in data
scarce regions, improve control on timing of collection, or when study areas
are to small to monitor via satellite. Automatic labelling is essential when
collecting large volumes of data. One such data collection is the EU's Land Use
Cover Area frame Survey (LUCAS), and in particular, the recently published
LUCAS Cover photos database. The aim of this paper is to select and publish a
subset of LUCAS Cover photos for 12 mature major crops across the EU, to
deploy, benchmark, and identify the best configuration of Mobile-net for the
classification task, to showcase the possibility of using entropy-based metrics
for post-processing of results, and finally to show the applications and
limitations of the model in a practical and policy relevant context. In
particular, the usefulness of automatically identifying crops on geo-tagged
photos is illustrated in the context of the EU's Common Agricultural Policy.
The work has produced a dataset of 169,460 images of mature crops for the 12
classes, out of which 15,876 were manually selected as representing a clean
sample without any foreign objects or unfavorable conditions. The best
performing model achieved a Macro F1 (M-F1) of 0.75 on an imbalanced test
dataset of 8,642 photos. Using metrics from information theory, namely - the
Equivalence Reference Probability, resulted in achieving an increase of 6%. The
most unfavorable conditions for taking such images, across all crop classes,
were found to be too early or late in the season. The proposed methodology
shows the possibility for using minimal auxiliary data, outside the images
themselves, in order to achieve a M-F1 of 0.817 for labelling between 12 major
European crops.
