  In this paper we report on improved part segmentation performance using
convolutional neural networks to reduce the dependency on the large amount of
manually annotated empirical images. This was achieved by optimising the visual
realism of synthetic agricultural images.In Part I, a cycle consistent
generative adversarial network was applied to synthetic and empirical images
with the objective to generate more realistic synthetic images by translating
them to the empirical domain. We first hypothesise and confirm that plant part
image features such as color and texture become more similar to the empirical
domain after translation of the synthetic images.Results confirm this with an
improved mean color distribution correlation with the empirical data prior of
0.62 and post translation of 0.90. Furthermore, the mean image features of
contrast, homogeneity, energy and entropy moved closer to the empirical mean,
post translation. In Part II, 7 experiments were performed using convolutional
neural networks with different combinations of synthetic, synthetic translated
to empirical and empirical images. We hypothesised that the translated images
can be used for (i) improved learning of empirical images, and (ii) that
learning without any fine-tuning with empirical images is improved by
bootstrapping with translated images over bootstrapping with synthetic images.
Results confirm our second and third hypotheses. First a maximum
intersection-over-union performance was achieved of 0.52 when bootstrapping
with translated images and fine-tuning with empirical images; an 8% increase
compared to only using synthetic images. Second, training without any empirical
fine-tuning resulted in an average IOU of 0.31; a 55% performance increase over
previous methods that only used synthetic images.
