In Namibia, the encroachment by a native, but invasive bush on Savannah land leads to both environmental and economic loss. This invasive bush is, however, suitable for harvesting as a source of biomass for local industry. Harvesting biomass from the invasive bush has been shown to restore biodiversity, improve water conservation efforts, and restore grazing lands. Beyond the environmental benefits of removing the invasive bush, the raw biomass harvested is amenable to simple value-added production. Although some efforts are underway to make use of harvested biomass, current harvesting practices are not selective enough to meet governmental requirements intended to protect several species of local fauna and flora. Limitations, such as a lack of knowledge, during the harvesting process, can be overcome to a significant degree through the introduction of a smart application. This can integrate geographical context with computer vision to provide a ground-level tool for the identification of areas suitable for harvesting. This study shows that this tool can identify indigenous taxonomies with an accuracy of 76 %. Keywords: computer vision, machine learning, random forest, encroachment.