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Plant phenotyping tasks such as leaf segmentation and counting arefundamental to the study of phenotypic traits. Since it is well-suited forthese tasks, deep supervised learning has been prevalent in recent worksproposing better performing models at segmenting and counting leaves. Despitegood efforts from research groups, one of the main challenges for proposingbetter methods is still the limitation of labelled data availability. The mainefforts of the field seem to be augmenting existing limited data sets, and someaspects of the modelling process have been under-discussed. This paper exploressuch topics and present experiments that led to the development of thebest-performing method in the Leaf Segmentation Challenge and in anotherexternal data set of Komatsuna plants. The model has competitive performancewhile been arguably simpler than other recently proposed ones. The experimentsalso brought insights such as the fact that model cardinality and test-timeaugmentation may have strong applications in object segmentation of singleclass and high occlusion, and regarding the data distribution of recentlyproposed data sets for benchmarking.

Leaf Segmentation and Counting with Deep Learning: on Model Certainty, Test-Time Augmentation, Trade-Offs