Redes neurais, identidade de modelos e resposta da cebola à adubação nitrogenada

• Autor(a) principal: Rodrigues, Dirceu Zeferino
• Data de Publicação: 2013
• Tipo de documento: Dissertação
• Idioma: por
• Título da fonte: LOCUS Repositório Institucional da UFV
• Texto Completo: http://locus.ufv.br/handle/123456789/4064
• topic: Cebola; Adubação nitrogenada; Redes neurais; Identidade de modelos; Onion; Nitrogen fertilization; Neural networks; Model identity; CNPQ::CIENCIAS AGRARIAS

The study of the productivity curves compared with the amount of nitrogen absorbed by the onion crop is fundamentally important for the elaboration of a more efficient fertilization plan in technical terms as well as in economic terms. Many statistical techniques have been proposed, tested, and improved in order to help boost research in this direction. The justification for this research is the need to assess and improve new statistical techniques that help in obtaining accurate information in order to assist in decision making for improving productivity. For this case, this study aimed to use and evaluate two statistical methods with different specific objectives with respect to the evaluation of nitrogen application in the production of onion cultivars. In the first evaluation, statistical techniques based on regression models were used for adjusting curves for some nitrogen levels related to productivity, performing a survey with four onion cultivars in different locations, and then to carry out the evaluation of the grouping possibility of these statistical models using the models identity test. In this step, it was tried to estimate a curve that could represent together the fertilization response pattern in all four evaluated sites. In the second study, the goal was to verify the techniques efficiency based on neural networks. So, the proposal was to see the possibility of using safely this new concept based on artificial neural networks in research related to the onion cultivars response to nitrogen fertilization. In general, this study describes the successful use of new statistical techniques with emphasis on neural networks that help improve the onion productivity and thereafter to implement and disseminate techniques based on computational intelligence for purposes of study prediction and modeling.