Geostatistics and remote sensing methods in the classification of images of areas cultivated with citrus

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Estudo da filogenia dos anofelinos brasileiros do subgênero Nyssorhynchus e padronização da amplificação dos genes do receptor do peptídeo sexual (SPR) e do peptídeo sexual (SP)

Pós-graduação em Ciências Biológicas (Genética) - IBB

Avaliação genética de caprinos da raça Saanen nascidos no Brasil de 1979 a 2009

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Inferência estatística clássica para a confiabilidade de rede de coautoria com enfoque nos vértices

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Estimativas de parâmetros genéticos para características de habilidade materna e reprodutivas em fêmeas da raça Santa Inês

Pós-graduação em Ciência e Tecnologia Animal - FEIS

Análise de pedigree e estimativas de parâmetros genéticos para características de importância econômica em bovinos Tabapuã

Pós-graduação em Genética e Melhoramento Animal - FCAV

Estimação de densidades e aplicação em segmentação de imagens

The aim of this work is to study some of the density estimation tec- niques and to apply to the segmentation of medical images. Medical images are used to help the diagnostic of tumor diseases as well as to plan and deliver treatment. A computer image is an array of values representing colors in some scale. The smallest element of the image to which it is possible to assign a value is called pixel. Segmen- tation is the process of dividing the image in portions through the classi¯cation of each pixel. The simplest way of classi¯cation is by thresholding, given the number of portions and the threshold values. Another method is constructing a histogram of the pixel values and assign a portion to each pike. The threshold is the mean between two pikes. As the histogram does not form a smooth curve it is di±cult to discern between true pikes and random variation. Density estimation methods allow the estimation of a smooth curve. Image data can be considered as mixture of different densities. In this project parametric and nonparametric methods for density estimation will be addressed and some of them are applied to CT image data

Divergência interpopulacional e interespecífica e processo seletivo atuante nos genes ortólogos a fruitless e Odysseus em Drosophila mojavensis e D. arizonae

Pós-graduação em Biologia Animal - IBILCE

Seleção de famílias e progênies de irmãos completos de cana-de-açúcar para atributos tecnológicos e de produção pelo método de REML/BLUP

The objective of this study was to assess families and highlight the superior progenies of sugarcane originating from 38 biparental crosses for the following attributes: tons of cane per hectare (TCH), tons of biomass per hectare (TBIOH), brix (% cane juice), fiber content, purity, pol and total recoverable sugar (TRS). The data were analyzed by mixed model REML / BLUP in the REML (Restricted Maximum Likelihood) allowed us to estimate genetic parameters and BLUP (best linear unbiased prediction) to predict the additive and genotypic values. The best family for the attributes TCH and TBIOH was 41, whose parents are cultivars IACSP022019 x CTC9. In individual selection for TCH, the plant number 3 of Block 2, the crossing 78, showed the best results. To TBIOH the plant number 33, Block 1, family 41, showed the best results. Families 40, 41, 43, 68, 69, 79, 91, 92 and 147, were higher for the variables brix, pol, purity, and ATR, where as 85 families, 147, 148, 149, 161, 163, 177, 178, 179, and 183 were higher for fiber. The family 147 whose parents are IACSP042286 x IACSP963055, showed three progenies ranked among the top ten for both brix, and for fiber, which identifies the combination as a potential source of progenies for bioenergy production.

Predição genômica para o peso ao sobreano via norma de reação em bovinos de corte

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Dinâmica populacional, distribuição espacial e plano de amostragem sequencial de Euschistus heros (Fabricius, 1794) (Heteroptera: Pentatomidae) em cultivares de soja

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Distribuição espacial, plano de amostragem sequencial e dinâmica populacional de Anthonomus grandis Boheman (Coleoptera: Curculionidae) em algodoeiro

Pós-graduação em Agronomia (Entomologia Agrícola) - FCAV

Comportamento de sistemas de informações geográficas por meio de classificação supervisionada em diferentes bacias hidrográficas

Pós-graduação em Agronomia (Energia na Agricultura) - FCA

Análise genética de escores visuais e sua relação com características reprodutivas de animais da raça Nelore

Pós-graduação em Genética e Melhoramento Animal - FCAV

Discriminação de classes de cobertura vegetal utilizando técnicas de classificação digital de imagens de sensoriamento remoto

The aim of this work is to discriminate vegetation classes throught remote sensing images from the satellite CBERS-2, related to winter and summer seasons in the Campos Gerais region Paraná State, Brazil. The vegetation cover of the region presents different kinds of vegetations: summer and winter cultures, reforestation areas, natural areas and pasture. Supervised classification techniques like Maximum Likelihood Classifier (MLC) and Decision Tree were evaluated, considering a set of attributes from images, composed by bands of the CCD sensor (1, 2, 3, 4), vegetation indices (CTVI, DVI, GEMI, NDVI, SR, SAVI, TVI), mixture models (soil, shadow, vegetation) and the two first main components. The evaluation of the classifications accuracy was made using the classification error matrix and the kappa coefficient. It was defined a high discriminatory level during the classes definition, in order to allow separation of different kinds of winter and summer crops. The classification accuracy by decision tree was 94.5% and the kappa coefficient was 0.9389 for the scene 157/128. For the scene 158/127, the values were 88% and 0.8667, respectively. The classification accuracy by MLC was 84.86% and the kappa coefficient was 0.8099 for the scene 157/128. For the scene 158/127, the values were 77.90% and 0.7476, respectively. The results showed a better performance of the Decision Tree classifier than MLC, especially to the classes related to cultivated crops, indicating the use of the Decision Tree classifier to the vegetation cover mapping including different kinds of crops.