  Crop mapping is one of the most common tasks in artificial intelligence for
agriculture due to higher food demands from a growing population and increased
awareness of climate change. In case of vineyards, the texture is very
important for crop segmentation: with higher resolution satellite imagery the
texture is easily detected by majority of state-of-the-art algorithms. However,
this task becomes increasingly more difficult as the resolution of satellite
imagery decreases and the information about the texture becomes unavailable. In
this paper we aim to explore the main machine learning methods that can be used
with freely available satellite imagery and discuss how and when they can be
applied for vineyard segmentation problem. We assess the effectiveness of
various widely-used machine learning techniques and offer guidance on selecting
the most suitable model for specific scenarios.
