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Intelligent Modeling of Sugar-Cane Maturation
Published by the American Society of Agricultural and Biological Engineers, St. Joseph, Michigan www.asabe.orgCitation: Computers in Agriculture and Natural Resources, 4th World Congress Conference, Proceedings of the 24-26 July 2006 (Orlando, Florida USA) Publication Date 24 July 2006 701P0606.(doi:10.13031/2013.21950)
Authors: Salomao Sampaio Madeiro, Flavio Rosendo da Silva Oliveira, Frederico Bruno Alves Alexandre, Fernando Buarque de Lima Neto
Keywords: Sugarcane, Harvest, Maturation, Artificial intelligence, Artificial neural networks, Genetic algorithms
Previous use of Artificial Intelligence (AI) in agriculture for forecasting productivity indicators, especially Artificial Neural Networks (ANN), has shown that it is possible to approximate sugarcane maturation curves. However, ANNs are widely known to offer some difficulties to be parameterized; normally, some heuristics and devotion by the users are necessary to provide a suitable parametrical selection. In this work we propose a tool that searches ANN parameters automatically (i.e. without direct interference of users in this task). For this high goal, we utilized another artificial intelligent technique: Genetic Algorithms (GA). The paper concludes showing results of ANNs which parameters have been provided by (the contributed) automated approach; comparisons to manually setup ANNs are included.(Download PDF) (Export to EndNotes)