Open AccessPrediction of Mechanical Availability in Mechanized Eucalyptus Forest Harvesting Using Artificial Neural Networks
Author(s) -
Leonardo Cassani Lacerda,
Edney Leandro da Vitória,
Nilton César Fiedler,
Flávio Cipriano de Assis do Carmo,
Saulo Boldrini Gonçalves,
Antônio Henrique Cordeiro Ramalho,
Diogo de Souza Alves
Publication year - 2022
Publication title -
journal of agricultural science
Language(s) - English
Resource type - Journals
eISSN - 1916-9760
pISSN - 1916-9752
DOI - 10.5539/jas.v14n3p157
Subject(s) - artificial neural network , backpropagation , eucalyptus , correlation coefficient , training (meteorology) , computer science , process (computing) , logging , environmental science , artificial intelligence , machine learning , geography , forestry , ecology , meteorology , biology , operating system
The planted forests in Brazil and in the world represent a significant slice of the forest sector in general, having the mechanization of activities, especially forest harvesting, is of great importance in the process. The objective was to estimate, through the use of Artificial Neural Networks, more reliable configurations to estimate the mechanical availability of harvester forest harvester-type equipment. The analyzed data were compiled and organized in a database of production monitoring of a company in the forest sector located in the southeast region of Brazil, later trained and validated according to neural network techniques. A trend was observed for the Resilient Propagation algorithm, where among all the trained ANNs, those that obtained the best R2 correlation values, the Quickpropagation training algorithm presented a correlation coefficient between the estimated values and observed values considered high, 0.9908, demonstrating that the trained networks are reliable. The Backpropagation training algorithm had a lower result, with only 75.77% of the estimated mechanical availability variation being explained by the observed mechanical availability. However, the application of artificial neural networks offers a practical solution to the problem of estimating mechanical availability quickly and accurately.