  An effective perception system is a fundamental component for farming robots,
as it enables them to properly perceive the surrounding environment and to
carry out targeted operations. The most recent methods make use of
state-of-the-art machine learning techniques to learn a valid model for the
target task. However, those techniques need a large amount of labeled data for
training. A recent approach to deal with this issue is data augmentation
through Generative Adversarial Networks (GANs), where entire synthetic scenes
are added to the training data, thus enlarging and diversifying their
informative content. In this work, we propose an alternative solution with
respect to the common data augmentation methods, applying it to the fundamental
problem of crop/weed segmentation in precision farming. Starting from real
images, we create semi-artificial samples by replacing the most relevant object
classes (i.e., crop and weeds) with their synthesized counterparts. To do that,
we employ a conditional GAN (cGAN), where the generative model is trained by
conditioning the shape of the generated object. Moreover, in addition to RGB
data, we take into account also near-infrared (NIR) information, generating
four channel multi-spectral synthetic images. Quantitative experiments, carried
out on three publicly available datasets, show that (i) our model is capable of
generating realistic multi-spectral images of plants and (ii) the usage of such
synthetic images in the training process improves the segmentation performance
of state-of-the-art semantic segmentation convolutional networks.
