  In this work, we investigate the application of existing unsupervised domain
adaptation (UDA) approaches to the task of transferring knowledge between crop
regions having different coffee patterns. Given a geographical region with
fully mapped coffee plantations, we observe that this knowledge can be used to
train a classifier and to map a new county with no need of samples indicated in
the target region. Experimental results show that transferring knowledge via
some UDA strategies performs better than just applying a classifier trained in
a region to predict coffee crops in a new one. However, UDA methods may lead to
negative transfer, which may indicate that domains are too different that
transferring knowledge is not appropriate. We also verify that normalization
affect significantly some UDA methods; we observe a meaningful complementary
contribution between coffee crops data; and a visual behavior suggests an
existent of a cluster of samples that are more likely to be drawn from a
specific data.
