Prediction of Mechanical Availability in Mechanized Eucalyptus Forest Harvesting Using Artificial Neural Networks

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.