Bressane, Adriano [UNESP]Frutuoso Roveda, Jose Arnaldo [UNESP]Germano Martins, Antonio Cesar [UNESP]Vellasco, MMBRValdivia, YJTLopes, H. S.2018-11-262018-11-262015-01-012015 Latin America Congress On Computational Intelligence (la-cci). New York: Ieee, 6 p., 2015.http://hdl.handle.net/11449/158983Tree species identification is required for many applications. However, current techniques are dependent on the presence of morphological structures such as leaves, which restricts its use in certain situations and seasons. In this context, the use of trunk images can be an alternative. Therefore, the present study developed a pattern recognition based on co-occurrence descriptors, aiming evaluate its performance in the identification of 8 tree species from the Brazilian deciduous native forest, achieving promising results, with precision better than 0.8 for most of them, accuracy equivalent to 0.77 and average area under curve by Receiver Operating Characteristic of 0.88, during the tests with cross-validation sets.6engImage processingDecision TreeCo-occurrence descriptorsTrunk imagesBrazilian forestTrabalho apresentado em eventoWOS:000380396300055Acesso aberto89596375594042060000-0002-4899-3983