  Deep learning techniques involving image processing and data analysis are
constantly evolving. Many domains adapt these techniques for object
segmentation, instantiation and classification. Recently, agricultural
industries adopted those techniques in order to bring automation to farmers
around the globe. One analysis procedure required for automatic visual
inspection in this domain is leaf count and segmentation. Collecting labeled
data from field crops and greenhouses is a complicated task due to the large
variety of crops, growth seasons, climate changes, phenotype diversity, and
more, especially when specific learning tasks require a large amount of labeled
data for training. Data augmentation for training deep neural networks is well
established, examples include data synthesis, using generative semi-synthetic
models, and applying various kinds of transformations. In this paper we propose
a method that preserves the geometric structure of the data objects, thus
keeping the physical appearance of the data-set as close as possible to imaged
plants in real agricultural scenes. The proposed method provides state of the
art results when applied to the standard benchmark in the field, namely, the
ongoing Leaf Segmentation Challenge hosted by Computer Vision Problems in Plant
Phenotyping.
