  We present an approach to leaf level segmentation of images of Arabidopsis
thaliana plants based upon detected edges. We introduce a novel approach to
edge classification, which forms an important part of a method to both count
the leaves and establish the leaf area of a growing plant from images obtained
in a high-throughput phenotyping system. Our technique uses a relatively
shallow convolutional neural network to classify image edges as background,
plant edge, leaf-on-leaf edge or internal leaf noise. The edges themselves were
found using the Canny edge detector and the classified edges can be used with
simple image processing techniques to generate a region-based segmentation in
which the leaves are distinct. This approach is strong at distinguishing
occluding pairs of leaves where one leaf is largely hidden, a situation which
has proved troublesome for plant image analysis systems in the past. In
addition, we introduce the publicly available plant image dataset that was used
for this work.
