  Manual determination of plant phenotypic properties such as plant
architecture, growth, and health is very time consuming and sometimes
destructive. Automatic image analysis has become a popular approach. This
research aims to identify the position (and number) of leaves from a temporal
sequence of high-quality indoor images consisting of multiple views, focussing
in particular of images of maize. The procedure used a segmentation on the
images, using the convex hull to pick the best view at each time step, followed
by a skeletonization of the corresponding image. To remove skeleton spurs, a
discrete skeleton evolution pruning process was applied. Pre-existing
statistics regarding maize development was incorporated to help differentiate
between true leaves and false leaves. Furthermore, for each time step, leaves
were matched to those of the previous and next three days using the
graph-theoretic Hungarian algorithm. This matching algorithm can be used to
both remove false positives, and also to predict true leaves, even if they were
completely occluded from the image itself. The algorithm was evaluated using an
open dataset consisting of 13 maize plants across 27 days from two different
views. The total number of true leaves from the dataset was 1843, and our
proposed techniques detect a total of 1690 leaves including 1674 true leaves,
and only 16 false leaves, giving a recall of 90.8%, and a precision of 99.0%.
