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Previous issue date: 2016-02-18 / Coordena??o de Aperfei?oamento de Pessoal de N?vel Superior, CAPES, Brasil. / The aim of this project was to develop a statistical classifier based on algorithms using
Bayesian theorem for prediction of nitrogen applied to the soil using low cost multispectral
images Tifton85 pastures From information obtained from these images the indexes were
calculated spectrum, which was used in a feature vector for discriminating N levels applied to the
soil. The research was developed in the Rural Federal University of Rio de Janeiro, Department of
Engineering, Machinery Area and Energy in Agriculture. The data were obtained from an
experiment installed in the production area of Company Feno Rio Agro LTDA, partner in the
project. Images were acquired in 2 times one after 36 days due to the cutting standardization of
culture using a UAV (Unmanned Aerial Vehicle) and at 7, 14, 21, 28, 35, 42, 56 days using an
imaging platform an experiment with 6 portions 250m2 (10x25m) containing respectively the
treatments (0, 50, 100, 150, 200 and 250 kg ha-? N), statistical analyzes were performed the
computer program SAS version student using PROCDISCRIM procedure. It was determined
discriminant functions for each dose of nitrogen, using as feature vectors vegetation indices
(NDVI, GNIR, SAVI, GNDVI, RNIR). These discriminant functions were tested in a Bayesian
classifier, and the highest accuracies were using vegetation indices together getting a Kappa
coefficient and overall accuracy of 83% and 80%, respectively. The results indicate that low cost
multispectral images can be used to develop classifiers of nitrogen applied to the soil. These
binders may be applied in computational algorithms in agricultural machinery control systems for
fertilizer application at variable rates in precision agriculture. / O objetivo geral deste projeto foi desenvolver um classificador estat?stico com base
em algoritmos utilizando o teorema bayesianos para predi??o de doses de nitrog?nio aplicadas
ao solo, atrav?s de imagens multiespectrais de baixo custo em pastagens de Tifton85 A partir
de informa??es obtidas nessas imagens foram calculados os ?ndices espectrais, os quais foram
utilizados em um vetor de caracter?sticas para discriminar n?veis de N aplicados ao solo. A
pesquisa foi desenvolvida na Universidade Federal Rural do Rio de Janeiro, Departamento de
Engenharia, ?rea de M?quinas e Energia na Agricultura. Os dados foram obtidos de um
experimento instalado no campo de produ??o da Empresa Feno Rio Agro LTDA, parceira do
projeto. As imagens foram adquiridas em 2 momentos uma ap?s 36 dias decorrentes ao corte
de uniformiza??o da cultura utilizando um VANT (Ve?culo A?reo N?o Tripulado) e aos 7, 14,
21, 28, 35, 42, 56 dias utilizando uma plataforma de capta??o de imagens de um experimento
com 6 parcelas contendo respectivamente os tratamentos (0, 50, 100, 150, 200 e 250 kg ha
-?
de N), as an?lises estat?sticas foram realizadas no programa computacional SAS vers?o
estudante, utilizando o procedimento PROCDISCRIM. Determinou-se fun??es discriminantes
para cada dose de nitrog?nio, utilizando-se como vetores de caracter?sticas os ?ndices de
vegeta??o (NDVI, GNIR, SAVI, GNDVI, RNIR). Essas fun??es discriminantes foram
testadas em um classificador bayesiano, sendo que a maior acur?cia de classifica??o foi
utilizando os ?ndices de vegeta??o em conjunto obtendo um coeficiente Kappa e a exatid?o
global de 83% e 80%, respectivamente. Os resultados indicam que imagens multiespectrais de
baixo custo podem ser utilizadas para desenvolver classificadores de doses de nitrog?nio
aplicadas ao solo. Esses classificadores poder?o ser aplicados em algoritmos computacionais
em sistemas de controle de m?quinas agr?colas para aplica??o de fertilizantes a taxas vari?veis
em agricultura de precis?o.
| Identifer | oai:union.ndltd.org:IBICT/oai:localhost:jspui/2198 |
| Date | 18 February 2016 |
| Creators | Silva, Ramon de Souza Victorino da |
| Contributors | Varella, Carlos Alberto Alves, Delgado, Rafael Coll, Zonta, Everaldo, Pinto, Francisco de Assis de Carvalho |
| Publisher | Universidade Federal Rural do Rio de Janeiro, Programa de P?s-Gradua??o em Engenharia Agr?cola e Ambiental, UFRRJ, Brasil, Instituto de Tecnologia |
| Source Sets | IBICT Brazilian ETDs |
| Language | Portuguese |
| Detected Language | English |
| Type | info:eu-repo/semantics/publishedVersion, info:eu-repo/semantics/masterThesis |
| Format | application/pdf |
| Source | reponame:Biblioteca Digital de Teses e Dissertações da UFRRJ, instname:Universidade Federal Rural do Rio de Janeiro, instacron:UFRRJ |
| Rights | info:eu-repo/semantics/openAccess |
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