  The use of deep learning methods for precision farming is gaining increasing
interest. However, collecting training data in this application field is
particularly challenging and costly due to the need of acquiring information
during the different growing stages of the cultivation of interest. In this
paper, we present a method for data augmentation that uses two GANs to create
artificial images to augment the training data. To obtain a higher image
quality, instead of re-creating the entire scene, we take original images and
replace only the patches containing objects of interest with artificial ones
containing new objects with different shapes and styles. In doing this, we take
into account both the foreground (i.e., crop samples) and the background (i.e.,
the soil) of the patches. Quantitative experiments, conducted on publicly
available datasets, demonstrate the effectiveness of the proposed approach. The
source code and data discussed in this work are available as open source.
