The forest inventory plays a crucial role in forest management planning, and it is the first step in planning actions for forest production. However, conducting an inventory can be expensive and complex. Forest inventory applications on smartphones have emerged as an alternative to traditional methods and they aim to make field data collection more accessible to non-professionals while ensuring accuracy in determining the volume of wood in a given area. This study evaluates the effectiveness of the Katam, Arboreal, and Trestima applications compared to traditional data collection methods. The study focuses on assessing the stand density and diameter of sampled trees—two key variables that are assessed in forest inventories. Two species, maritime pine (Pinus pinaster Aiton) and Eucalyptus spp. (mainly Eucalyptus globulus and Eucalyptus nitens), were used to evaluate the performance of the methods, with assessments performed in the stands of diverse dendrometric characteristics, specifically those regarding the tree age, stand density, and topographic conditions (flat or sloping terrain). For the purpose of comparison, goodness-of-fit statistics (R², RMSE, and BIAS) were calculated, and an analysis of the diameter distribution and comparison of the mean diameter, number of trees per hectare, and basal area were performed. In general, the applications were accurate, and the average basal area did not differ significantly from the traditional method. The diameter measurements showed good accuracy. The accuracy of the applications varied depending on the terrain and forest characteristics, with the applications performing better in areas with flat terrain, as well as with older forests that were regular and had low under-cover density. In contrast, the applications performed worse in younger, irregular forests with sloping terrain, high tree density, and those with a great deal of understory vegetation. The applications still need to evolve in evaluating other important variables (such as tree height or volume) as they are currently estimated from auxiliary variables through mathematical equations.