Artificial neural networks applied in forest biometrics and modeling: state of the art (January/2007 to July/2018).

• Autor(a) principal: CHIARELLO, F.
• Data de Publicação: 2019
• Outros Autores: STEINER, M. T. A., OLIVEIRA, E. B. de, ARCE, J. E., FERREIRA, J. C.
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice)
• Texto Completo: http://www.alice.cnptia.embrapa.br/alice/handle/doc/1115699
• topic: Bibliometric Review; Multilayer Perceptron; Forest Engineering Problems; Revisão sistemática; Revisão Bibliométrica; Inteligência artificial; Artificial intelligence; Systematic review

Artificial Intelligence has been an important support tool in different spheres of activity, enabling knowledge aggregation, process optimization and the application of methodologies capable of solving complex real problems. Despite focusing on a wide range of successful metrics, the Artificial Neural Network (ANN) approach, a technique similar to the central nervous system, has gained notoriety and relevance with regard to the classification of standards, intrinsic parameter estimates, remote sense, data mining and other possibilities. This article aims to conduct a systematic review, involving some bibliometric aspects, to detect the application of ANNs in the field of Forest Engineering, particularly in the prognosis of the essential parameters for forest inventory, analyzing the construction of the scopes, implementation of networks (type ? classification), the software used and complementary techniques. Of the 1,140 articles collected from three research databases (Science Direct, Scopus and Web of Science), 43 articles underwent these analyses. The results show that the number of works within this scope has increased continuously, with 32% of the analyzed articles predicting the final total marketable volume, 78% making use of Multilayer Perceptron Networks (MLP, Multilayer Perceptron) and 63% from Brazilian researchers.