  The task of counting eucalyptus trees from aerial images collected by
unmanned aerial vehicles (UAVs) has been frequently explored by techniques of
estimation of the basal area, i.e, by determining the expected number of trees
based on sampling techniques. An alternative is the use of machine learning to
identify patterns that represent a tree unit, and then search for the
occurrence of these patterns throughout the image. This strategy depends on a
supervised image segmentation step to define predefined interest regions. Thus,
it is possible to automate the counting of eucalyptus trees in these images,
thereby increasing the efficiency of the eucalyptus forest inventory
management. In this paper, we evaluated 20 different classifiers for the image
segmentation task. A real sample was used to analyze the counting trees task
considering a practical environment. The results show that it possible to
automate this task with 0.7% counting error, in particular, by using strategies
based on a combination of classifiers. Moreover, we present some performance
considerations about each classifier that can be useful as a basis for
decision-making in future tasks.
