Abstract
Artificial Intelligence has been an important support tool in different spheres of activity, enabling the knowledge aggregation, the process optimization and the application of methodologies capable of solving complex real problems. Although focusing on a wide space of successful metrics, the Artificial Neural Networks (ANN) approach, a technique similar to the central nervous system, has been gaining notoriety and relevance with regard to the classification of standards, intrinsic parameter estimates, remote sense, data mining and other possibilities. This article aims to develop a systematic review, involving some bibliometric aspects, aimed at detecting the application of ANNs in the field of Forest Engineering, particularly in the prognosis of the essential parameters to the forest inventory, analyzing the construction of the scopes, implementation of networks (type – classification), the software used and complementary techniques. From the 1,140 articles collected in three search databases (Science Direct, Scopus and Web of Science), 43 articles underwent such analyzes. The results show that the number of works within this scope has been increasing continuously, and 32% of the analyzed articles predict the final total marketable volume; 78% made use of the Multilayer Perceptron Networks (MLP, Multilayer Perceptron) and 63% were from Brazilian researchers.
Highlights
The human brain has attributes that would be desirable in any artificial system
Initially, the analysis focused on the production of published articles, with emphasis on the application of Artificial Neural Network (ANN) in the forest context
Considering the interpretation of the objectives proposed by each author, some authors chose to deepen their studies with more than one aim, often due to the possibility of finding an alternative tool to the traditional techniques or models that are frequently used in the forest scenario
Summary
The human brain has attributes that would be desirable in any artificial system. Its skills in dealing with probabilistic and/or inconsistent information in different situations, and its flexibility to adapt to poorly defined situations, has attracted the attention of many scholars, who intensified their research in the field of artificial intelligence (AI) in the 1980s with the use of intensive computing.The dissemination of the methodologies found in this field has achieved interesting results in different areas of knowledge. Its skills in dealing with probabilistic and/or inconsistent information in different situations, and its flexibility to adapt to poorly defined situations, has attracted the attention of many scholars, who intensified their research in the field of artificial intelligence (AI) in the 1980s with the use of intensive computing. Studies guarantee that in different scenarios ANNs have contributed to high performance compared with classical regression models. Their purely massive structure, distributed (in layers) and ability to learn and generalize situations, tolerance of flaws and noises, and their flexibility in modeling categorical (qualitative) and numerical variables provide the methodology with a favorable context regarding the capacity to solve problems of any size (Binoti et al, (2013)
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