  Objectives. We generate via advanced Deep Learning (DL) techniques artificial
leaf images in an automatized way. We aim to dispose of a source of training
samples for AI applications for modern crop management. Such applications
require large amounts of data and, while leaf images are not truly scarce,
image collection and annotation remains a very time--consuming process. Data
scarcity can be addressed by augmentation techniques consisting in simple
transformations of samples belonging to a small dataset, but the richness of
the augmented data is limited: this motivates the search for alternative
approaches. Methods. Pursuing an approach based on DL generative models, we
propose a Leaf-to-Leaf Translation (L2L) procedure structured in two steps:
first, a residual variational autoencoder architecture generates synthetic leaf
skeletons (leaf profile and veins) starting from companions binarized skeletons
of real images. In a second step, we perform translation via a Pix2pix
framework, which uses conditional generator adversarial networks to reproduce
the colorization of leaf blades, preserving the shape and the venation pattern.
Results. The L2L procedure generates synthetic images of leaves with a
realistic appearance. We address the performance measurement both in a
qualitative and a quantitative way; for this latter evaluation, we employ a DL
anomaly detection strategy which quantifies the degree of anomaly of synthetic
leaves with respect to real samples. Conclusions. Generative DL approaches have
the potential to be a new paradigm to provide low-cost meaningful synthetic
samples for computer-aided applications. The present L2L approach represents a
step towards this goal, being able to generate synthetic samples with a
relevant qualitative and quantitative resemblance to real leaves.
