  Machine learning (ML) methods and neural networks (NN) are widely implemented
for crop types recognition and classification based on satellite images.
However, most of these studies use several multi-temporal images which could be
inapplicable for cloudy regions. We present a comparison between the classical
ML approaches and U-Net NN for classifying crops with a single satellite image.
The results show the advantages of using field-wise classification over
pixel-wise approach. We first used a Bayesian aggregation for field-wise
classification and improved on 1.5% results between majority voting
aggregation. The best result for single satellite image crop classification is
achieved for gradient boosting with an overall accuracy of 77.4% and macro
F1-score 0.66.
