  Plant phenotyping tasks such as leaf segmentation and counting are
fundamental to the study of phenotypic traits. Since it is well-suited for
these tasks, deep supervised learning has been prevalent in recent works
proposing better performing models at segmenting and counting leaves. Despite
good efforts from research groups, one of the main challenges for proposing
better methods is still the limitation of labelled data availability. The main
efforts of the field seem to be augmenting existing limited data sets, and some
aspects of the modelling process have been under-discussed. This paper explores
such topics and present experiments that led to the development of the
best-performing method in the Leaf Segmentation Challenge and in another
external data set of Komatsuna plants. The model has competitive performance
while been arguably simpler than other recently proposed ones. The experiments
also brought insights such as the fact that model cardinality and test-time
augmentation may have strong applications in object segmentation of single
class and high occlusion, and regarding the data distribution of recently
proposed data sets for benchmarking.
