Streamlining the study of the Tierra del Fuego forest through the use of deep learning

Abstract

Understanding plant-herbivorous relationships allows to optimize the way to manage and protect natural spaces. In this paper the study of this relationship in the ñire forests (Nothofagus antarctica) of the province of Tierra del Fuego (Argentina) is approached. Using trap cameras to monitor such interaction offers the opportunity to quickly collect large amounts of data. However, to take advantage of its potential, a large investment in trained personnel to analyze and filter the images of interest is required. The present work seeks to establish a path to significantly reduce this obstacle using the advances of machine and deep learning in the recognition of objects from images.