Prediction leaf area in acerola by neural networks and multiple regression
Alcinei Místico de Azevedo, Vitor Alves da Silveira, Celso Mattes Oliveira, Carlos Enrrik Pedrosa, Vinícius Teixeira Lemos, Nermy Ribeiro Valadares, Amanda Gonçalves Guimarães
Journal Title:Revista Agraria Academica / Agrarian Academic Journal
The objective of this work was to predict the leaf area in acerola by means of artificial neural networks (ANNs) and verify the efficiency of this methodology in comparison to multiple regression models. The length, width and area of 350 leaves of acerola were evaluated, 14 models of multiple regression and model of multilayer perceptron type RNA were used to predict the leaf area. The quality of fitbetween the multiple regression models and the ANNs was close, but the artificial neural networks were more efficient in the prediction of the leaf area in acerola, with determination coefficient superior to0,98, being the network with two neurons in theintermediate layer the best prediction.