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Registros recuperados: 21
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Annual cropland mapping using data mining and OLI Landsat-8. Repositório Alice
OLDONI, L. V.; CATTANI, C. E. V.; MERCANTE, E.; JOHANN, J. A.; ANTUNES, J. F. G.; ALMEIDA, L..
ABSTRACT: In the state of Paraná, Brazil, there are no major changes in areas cultivated with annual crops, mainly due to environmental laws that do not allow expansions to new areas. There is a great contribution of the annual crops to the domestic demand of food and economic demand in the exports. Thus, the area and distribution of annual crops are information of great importance. New methodologies, such as data mining, are being tested with the objective of analyzing and improving their potential use for classification of land use and land cover. This study used the classifiers decision tree and random forest with Normalized Difference Vegetation Index (NDVI) temporal metrics on images from Operational Land Imager (OLI)/Landsat-8. The results were...
Tipo: Artigo em periódico indexado (ALICE) Palavras-chave: Árvore de decisão; Métricas temporais de NDVI; Mineração de dados; Séries temporais; Decision tree; NDVI temporal metrics; Random forest; Data mining; Normalized difference vegetation index; Time series analysis.
Ano: 2019 URL: http://www.alice.cnptia.embrapa.br/alice/handle/doc/1114915
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Annual cropland mapping using data mining and OLI Landsat-8 AGRIAMBI
Oldoni,Lucas V.; Cattani,Carlos E. V.; Mercante,Erivelto; Johann,Jerry A.; Antunes,João F. G.; Almeida,Luiz.
ABSTRACT In the state of Paraná, Brazil, there are no major changes in areas cultivated with annual crops, mainly due to environmental laws that do not allow expansions to new areas. There is a great contribution of the annual crops to the domestic demand of food and economic demand in the exports. Thus, the area and distribution of annual crops are information of great importance. New methodologies, such as data mining, are being tested with the objective of analyzing and improving their potential use for classification of land use and land cover. This study used the classifiers decision tree and random forest with Normalized Difference Vegetation Index (NDVI) temporal metrics on images from Operational Land Imager (OLI)/Landsat-8. The results were...
Tipo: Info:eu-repo/semantics/article Palavras-chave: Decision tree; Random forest; NDVI temporal metrics.
Ano: 2019 URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S1415-43662019001200952
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Associação de SNPs com características de carcaça em uma população da raça Canchim. Repositório Alice
MOKRY, F. B.; LIMA, A. O. de; URBINATI, I.; TORRES JUNIOR, R. A. de A.; HIGA, R. H.; REGITANO, L. C. de A..
Resumo: Foram genotipados 400 animais Canchim que apresentavam fenótipos para área de olho de lombo (AOL) e espessura de gordura subcutânea (EGS). Após o controle de qualidade, foi realizado análise de associação dos SNPs com AOL e EGS por meio da metodologia de RandomForest. Os 400 indivíduos foram também reamostrados 10 vezes formando grupos de 200 indivíduos mais representativos e menos aparentados. Foi criado um grupo de SNPs contendo apenas os SNPs que foram selecionados em todas as análises, considerados os SNPs mais robustos totalizando 197 SNPs para AOL e 162 SNPs para EGS. Após o ajuste de uma regressão selecionou-se um conjunto de 20 SNPs para AOL com R2=0,59 e um conjunto de 17 SNPs para EGS com R2=0,49. Esses SNPs ainda precisam ser validados...
Tipo: Artigo em anais de congresso (ALICE) Palavras-chave: Área de olho de lombo; Bovino de corte; Espessura de gordura subcutânea; Random forest; Ribeye area; Backfat; Single nucleotide polymorphism; Beef cattle.
Ano: 2012 URL: http://www.alice.cnptia.embrapa.br/handle/doc/946729
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Biometric characteristics and canopy reflectance association for early-stage sugarcane. Repositório Alice
ROCHA, M. G. da; BARROS, F. M. M. de; OLIVEIRA, S. R. de M.; AMARAL, L. R. do.
ABSTRACT: Knowing the spatial variability of sugarcane biomass in the early stages of development may help growers in their management decision-making. Proximal canopy sensing is a promising technology that can identify this variability but is limited to quantifying plant-specific parameters. In this study, we evaluated whether biometric variables integrated with canopy reflectance data can assist in the generation of models for early-stage sugarcane biomass prediction. To substantiate this assertion, four sugarcane-producing fields were measured with an active crop canopy sensor and 30 sampling plots were selected for manually quantifying chlorophyll content, plant height, stalk number and aboveground biomass. We determined that Random Forest and Multiple...
Tipo: Artigo em periódico indexado (ALICE) Palavras-chave: Floresta aleatória; Índice de vegetação; Mineração de dados; Precision farming; Random forest; Vegetation indices; Data mining; Canopy sensor; Biomassa; Cana de Açúcar; Agricultura de Precisão; Biomass; Sugarcane; Precision agriculture; Vegetation index.
Ano: 2019 URL: http://www.alice.cnptia.embrapa.br/alice/handle/doc/1110823
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Biometric characteristics and canopy reflectance association for early-stage sugarcane biomass prediction Scientia Agricola
Rocha,Murillo Grespan da; Barros,Flávio Margarito Martins de; Oliveira,Stanley Robson de Medeiros; Amaral,Lucas Rios do.
ABSTRACT: Knowing the spatial variability of sugarcane biomass in the early stages of development may help growers in their management decision-making. Proximal canopy sensing is a promising technology that can identify this variability but is limited to quantifying plant-specific parameters. In this study, we evaluated whether biometric variables integrated with canopy reflectance data can assist in the generation of models for early-stage sugarcane biomass prediction. To substantiate this assertion, four sugarcane-producing fields were measured with an active crop canopy sensor and 30 sampling plots were selected for manually quantifying chlorophyll content, plant height, stalk number and aboveground biomass. We determined that Random Forest and Multiple...
Tipo: Info:eu-repo/semantics/article Palavras-chave: Random forest; Canopy sensor; Vegetation indices; Precision farming; Data mining.
Ano: 2019 URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0103-90162019001400274
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Estimation of elemental composition of agricultural soils from West Azerbaijan, Iran, using mid-infrared spectral models AGRIAMBI
Goydaragh,Maryam Ghebleh; Jafarzadeh,Ali Asghar; Shahbazi,Farzin; Oustan,Shahin; Taghizadeh-Mehrjardi,Ruhollah; Lado,Marcos.
ABSTRACT Characterizing the elemental composition provides useful information about the weathering degree of soils. In Miandoab County, Northern Iran, this characterization was missing, and thus the objectives of this work were to evaluate the weathering degrees for the most typical soils in the area from their elemental compositions, and to estimate this elemental composition using Fourier transform infrared spectroscopy and Random Forest models. Five soil profiles, including Aridisols and Inceptisols, were selected as the most representative of the area. Major elemental oxides were determined in each genetic horizon by X-ray fluorescence, showing that these soils were at early developmental stages. Only Al2O3 and CaO were accurately estimated, with R2...
Tipo: Info:eu-repo/semantics/article Palavras-chave: FTIR spectra; Random forest; Weathering.
Ano: 2019 URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S1415-43662019000600460
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Exploring Machine Learning to Correct Satellite-Derived Sea Surface Temperatures ArchiMer
Picart, Stephane Saux; Tandeo, Pierre; Autret, Emmanuelle; Gausset, Blandine.
Machine learning techniques are attractive tools to establish statistical models with a high degree of non linearity. They require a large amount of data to be trained and are therefore particularly suited to analysing remote sensing data. This work is an attempt at using advanced statistical methods of machine learning to predict the bias between Sea Surface Temperature (SST) derived from infrared remote sensing and ground “truth” from drifting buoy measurements. A large dataset of collocation between satellite SST and in situ SST is explored. Four regression models are used: Simple multi-linear regression, Least Square Shrinkage and Selection Operator (LASSO), Generalised Additive Model (GAM) and random forest. In the case of geostationary satellites for...
Tipo: Text Palavras-chave: Machine learning; Systematic error; Sea surface temperature; Random forest.
Ano: 2018 URL: https://archimer.ifremer.fr/doc/00426/53797/54721.pdf
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Mapeamento digital da CTC em solos do semiárido brasileiro. Repositório Alice
CHAGAS, C. da S.; CARVALHO JUNIOR, W. de; BHERING, S. B.; PEREIRA, N. R..
O planejamento de uso e o manejo sustentável da terra requerem informações confiáveis sobre a distribuição espacial de propriedades físicas e químicas do solo que afetam os processos e serviços na paisagem. Neste sentido, a capacidade de troca catiônica (CTC) é uma das propriedades do solo mais importantes, sendo considerada como um indicador vital da qualidade do solo, porém, sua obtenção é cara e demorada. Embora muitos estudos tenham sido conduzidos para identificar o padrão espacial da distribuição de propriedades do solo em várias escalas e várias paisagens, pouco se conhece sobre as relações existentes entre estas propriedades e diferentes covariáveis ambientais no semiárido brasileiro. Sendo assim, o objetivo desse estudo foi avaliar a eficiência...
Tipo: Artigo em anais de congresso (ALICE) Palavras-chave: Mapeamento digital de solos; Random forest; Cokrigagem.
Ano: 2015 URL: http://www.alice.cnptia.embrapa.br/handle/doc/1031631
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Mapeamento digital de atributos físicos e físico-hídricos do solo por técnicas de mineração de dados. Repositório Alice
CHAGAS, C. da S.; BHERING, S. B.; CARVALHO JUNIOR, W. de; PEREIRA, N. R..
O presente estudo tem por objetivo comparar a eficiência dos modelos AR, RF e RNA na predição de atributos físicos e físico-hídricos do solo utilizando dados do sensor TM do Landsat 5 como covariáveis ambientais em uma área do semiárido brasileiro.
Tipo: Artigo em anais de congresso (ALICE) Palavras-chave: Árvores de regressão; Random forest; Redes neurais artificiais.
Ano: 2016 URL: http://www.alice.cnptia.embrapa.br/handle/doc/1059787
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Microbiological Indicators of Soil Quality Under Native Forests are Influenced by Topographic Factors Anais da ABC (AABC)
TEIXEIRA,ANITA F.S.; SILVA,JACQUELINE S.; VILELA,LAÍZE A.F.; COSTA,PATRÍCIA F.; COSTA,ELAINE M. DA; GUIMARÃES,AMANDA A.; SANTOS,JESSÉ V. DOS; SILVA,SÉRGIO H.G.; CARNEIRO,MARCO AURÉLIO C.; MOREIRA,FATIMA M.S..
Abstract: Several microbiological indicators of soil quality present high sensitivity, but little is known about the influence of topographic factors on them. This work aimed to evaluate variability of biological indicators of soil quality across a hillslope under native forest and the influence of topographic factors on them. Four positions on a hillslope were evaluated. Activity of the enzymes β-glucosidase, acid phosphatase, urease and fluorescein diacetate (FDA) hydrolysis were determined, as well as basal and substrate-induced respiration, and density of microorganisms: total bacteria, total fungi, actinobacteria, phosphate solubilizers, ammonifiers, native rhizobia, free-living N2-fixing bacteria, spores of arbuscular mycorrhizal fungi and percentage...
Tipo: Info:eu-repo/semantics/article Palavras-chave: Biological indicators; Digital elevation model; Enzymes; Microbial activity; Random forest.
Ano: 2019 URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0001-37652019000700866
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Modeling and prediction of sulfuric acid digestion analyses data from PXRF spectrometry in tropical soils Scientia Agricola
Silva,Sérgio Henrique Godinho; Silva,Elen Alvarenga; Poggere,Giovana Clarice; Pádua Junior,Alceu Linares; Gonçalves,Mariana Gabriele Marcolino; Guilherme,Luiz Roberto Guimarães; Curi,Nilton.
ABSTRACT Sulfuric acid digestion analyses (SAD) provide useful information to environmental studies, in terms of the geochemical balance of nutrients, parent material uniformity, nutrient reserves for perennial crops, and mineralogical composition of the soil clay fraction. Yet, these analyses are costly, time consuming, and generate chemical waste. This work aimed at predicting SAD results from portable X-ray fluorescence (pXRF) spectrometry, which is proposed as a “green chemistry” alternative to the current SAD method. Soil samples developed from different parent materials were analyzed for soil texture and SAD, and scanned with pXRF. The SAD results were predicted from pXRF elemental analyses through simple linear regressions, stepwise multiple linear...
Tipo: Info:eu-repo/semantics/article Palavras-chave: Soil clay fraction; Weathering indices; Random forest; Proximal sensors; Green chemistry.
Ano: 2020 URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0103-90162020000401402
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Modeling forest aboveground carbon density in the Brazilian Amazon with integration of MODIS and Airborne LiDAR data. Repositório Alice
JIANG, X.; LI, G.; LU, D.; MORAN, E.; BATISTELLA, M..
Abstract: Timely updates of carbon stock distribution are needed to better understand the impacts of deforestation and degradation on forest carbon stock dynamics. This research aimed to explore an approach for estimating aboveground carbon density (ACD) in the Brazilian Amazon through integration of MODIS (moderate resolution imaging spectroradiometer) and a limited number of light detection and ranging (Lidar) data samples using linear regression (LR) and random forest (RF) algorithms, respectively. Airborne LiDAR data at 23 sites across the Brazilian Amazon were collected and used to calculate ACD. The ACD estimation model, which was developed by Longo et al. in the same study area, was used to map ACD distribution in the 23 sites. The LR and RF methods...
Tipo: Artigo em periódico indexado (ALICE) Palavras-chave: Densidade de carbono acima do solo; Floresta aleatória; Amazônia brasileira; Random forest; MODIS; Brazilian Amazon; Linear regression; Aboveground carbon density; Regressão Linear; Biomassa; Aboveground biomass; Carbon; Lidar.
Ano: 2020 URL: http://www.alice.cnptia.embrapa.br/alice/handle/doc/1126323
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Modeling of stem form and volume through machine learning Anais da ABC (AABC)
SCHIKOWSKI,ANA B.; CORTE,ANA P.D.; RUZA,MARIELI S.; SANQUETTA,CARLOS R.; MONTAÑO,RAZER A.N.R..
Abstract Taper functions and volume equations are essential for estimation of the individual volume, which have consolidated theory. On the other hand, mathematical innovation is dynamic, and may improve the forestry modeling. The objective was analyzing the accuracy of machine learning (ML) techniques in relation to a volumetric model and a taper function for acácia negra. We used cubing data, and fit equations with Schumacher and Hall volumetric model and with Hradetzky taper function, compared to the algorithms: k nearest neighbor (k-NN), Random Forest (RF) and Artificial Neural Networks (ANN) for estimation of total volume and diameter to the relative height. Models were ranked according to error statistics, as well as their dispersion was verified....
Tipo: Info:eu-repo/semantics/article Palavras-chave: Artificial intelligence; Data mining; Random forest; ANN.
Ano: 2018 URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0001-37652018000703389
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Monitoring pasture aboveground biomass and canopy height in an integrated crop-livestock system using textural information from PlanetScope imagery. Repositório Alice
REIS, A. A. dos; WERNER, J. P. S.; SILVA, B. C.; FIGUEIREDO, G. K. D. A.; ANTUNES, J. F. G.; ESQUERDO, J. C. D. M.; COUTINHO, A. C.; LAMPARELLI, R. A. C.; ROCHA, J. V.; MAGALHÃES, P. S. G..
Abstract: Fast and accurate quantification of the available pasture biomass is essential to support grazing management decisions in intensively managed fields. The increasing temporal and spatial resolutions oered by the new generation of orbital platforms, such as Planet CubeSat satellites, have improved the capability of monitoring pasture biomass using remotely sensed data. Here, we assessed the feasibility of using spectral and textural information derived from PlanetScope imagery for estimating pasture aboveground biomass (AGB) and canopy height (CH) in intensively managed fields and the potential for enhanced accuracy by applying the extreme gradient boosting (XGBoost) algorithm. Our results demonstrated that the texture measures enhanced AGB and CH...
Tipo: Artigo em periódico indexado (ALICE) Palavras-chave: Pasto; Pastagem tropical; Floresta aleatória; Random forest; Mixed pastures; Integrated systems; Texture measures; Extreme gradient boosting; Biomassa; Pastagem Mista; Sensoriamento Remoto; Pastures; Tropical pastures; Biomass; Aboveground biomass; Remote sensing.
Ano: 2020 URL: http://www.alice.cnptia.embrapa.br/alice/handle/doc/1125026
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Potencial de técnicas de mineração de dados para modelos de alerta da ferrugem do cafeeiro. Repositório Alice
DI GIROLAMO NETO, C.; RODRIGUES, L. H. A.; THAMADA, T. T.; MEIRA, C. A. A..
Resumo. Este trabalho procurou avaliar o potencial de técnicas de mineração de dados no desenvolvimento de modelos de alerta da ferrugem do cafeeiro. Foram avaliadas quatro técnicas: Redes Neurais Artificiais, Árvores de Decisão, Support Vector Machines e Random Forest. A avaliação dos modelos gerados mostrou que as duas últimas técnicas geram modelos com maior taxa de acerto e melhores medidas de sensitividade e especificidade. As Redes Neurais Artificiais geraram modelos com alto valor de sensitividade, enquanto que as Árvores de Decisão obtiveram desempenho inferior quando comparadas às demais técnicas. O balanceamento de classes se mostrou um procedimento fundamental na melhora da taxa de acerto dos modelos.
Tipo: Artigo em anais de congresso (ALICE) Palavras-chave: Mineração de dados; Modelos de alerta; Ferrugem do cafeeiro; Redes neurais; Árvore de decisão; Data mining; Decision tree; Random forest; Support vector machines; Coffee rust; Neural networks.
Ano: 2013 URL: http://www.alice.cnptia.embrapa.br/handle/doc/971782
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Preprocessing procedures and supervised classification applied to a database of systematic soil survey. Repositório Alice
VALADARES, A. P.; COELHO, R. M.; OLIVEIRA, S. R. de M..
ABSTRACT:Data Mining techniques play an important role in the prediction of soil spatial distribution in systematic soil surveying, though existing methodologies still lack standardization and a full understanding of their capabilities. The aim of this work was to evaluate the performance of preprocessing procedures and supervised classification approaches for predicting map units from 1:100,000-scale conventional semi-detailed soil surveys. Sheets of the Brazilian National Cartographic System on the 1:50,000 scale, ?Dois Córregos? (?Brotas? 1:100,000-scale sheet), ?São Pedro? and ?Laras? (?Piracicaba? 1:100,000-scale sheet) were used for developing models. Soil map information and predictive environmental covariates for the dataset were obtained from the...
Tipo: Artigo em periódico indexado (ALICE) Palavras-chave: Aprendizado de máquina; Pré-processamento; Classificação de solos; Random forest; Machine learning algorithms; Tacit soil-landscape relationships; Digital soil mapping; Solo; Soil; Soil classification.
Ano: 2019 URL: http://www.alice.cnptia.embrapa.br/alice/handle/doc/1118563
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Preprocessing procedures and supervised classification applied to a database of systematic soil survey Scientia Agricola
Valadares,Alan Pessoa; Coelho,Ricardo Marques; Oliveira,Stanley Robson de Medeiros.
ABSTRACT: Data Mining techniques play an important role in the prediction of soil spatial distribution in systematic soil surveying, though existing methodologies still lack standardization and a full understanding of their capabilities. The aim of this work was to evaluate the performance of preprocessing procedures and supervised classification approaches for predicting map units from 1:100,000-scale conventional semi-detailed soil surveys. Sheets of the Brazilian National Cartographic System on the 1:50,000 scale, “Dois Córregos” (“Brotas” 1:100,000-scale sheet), “São Pedro” and “Laras” (“Piracicaba” 1:100,000-scale sheet) were used for developing models. Soil map information and predictive environmental covariates for the dataset were obtained from the...
Tipo: Info:eu-repo/semantics/article Palavras-chave: Machine learning algorithms; Random forest; Tacit soil-landscape relationships; Digital soil mapping.
Ano: 2019 URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0103-90162019001500439
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Sample design effects on soil unit prediction with machine: randomness, uncertainty, and majority map. Repositório Alice
CARVALHO JUNIOR, W. de; PEREIRA, N. R.; FERNANDES FILHO, E. I.; CALDERANO FILHO, B.; PINHEIRO, H. S. K.; CHAGAS, C. da S.; BHERING, S. B.; PEREIRA, V. R.; LAWALL, S..
Notwithstanding the importance of soil surveys, advances in digital soil mapping have mainly focused on mapping soil attributes or properties rather than developing digital maps of soil units or soil classes. The purpose of this research was to develop digital soil unit maps based on primary soil data collection in areas without previously collected soil information. The covariate variability, the random effect across the data subset and the map outputs were the focuses of this study. We used five datasets with four models (Random Forest - RF, Gradient Boosted Machine - GBM, C5.0, and multinomial log-linear model - MLR). The covariates were grouped into five datasets, where four were grouped by Region Of Interest per Class (ROIC) and one was not grouped by...
Tipo: Artigo em periódico indexado (ALICE) Palavras-chave: Mapeamento digital de solos; Tree learners models; Hillslope areas; Random forest; Mapa; Solo; Soil map.
Ano: 2020 URL: http://www.alice.cnptia.embrapa.br/alice/handle/doc/1124458
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Self-guided segmentation and classification of multi-temporal landsat 8 images for crop type mapping in southeastern Brazil. Repositório Alice
SCHULTZ, B.; IMMITZER, M.; FORMAGGIO, A. R.; SANCHES, I. D. A.; LUIZ, A. J. B.; ATZBERGER, C..
Abstract: Only well-chosen segmentation parameters ensure optimum results of object-based image analysis (OBIA). Manually defining suitable parameter sets can be a time-consuming approach, not necessarily leading to optimum results; the subjectivity of the manual approach is also obvious. For this reason, in supervised segmentation as proposed by Stefanski et al. (2013) one integrates the segmentation and classification tasks. The segmentation is optimized directly with respect to the subsequent classification. In this contribution, we build on this work and developed a fully autonomous workflow for supervised object-based classification, combining image segmentation and random forest (RF) classification. Starting from a fixed set of randomly selected and...
Tipo: Artigo em periódico indexado (ALICE) Palavras-chave: Mapeamento agrícola; Segmentação multirresolução; OBIA; Crop mapping; Brazil; Multi-resolution segmentation; OLI; Random forest; Sensoriamento remoto; Remote sensing.
Ano: 2015 URL: http://dx.doi.org/10.3390/rs71114482
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Uso de geotecnologias para a detecção de sistemas integrados de produção agropecuária: uma contribuição para o monitoramento da agricultura de baixa emissão de carbono. Repositório Alice
KUCHLER, P. C.; SIMÕES, M.; FERRAZ, R. P. D.; BÉGUÉ, A..
A implementação dos Sistemas Integrados de produção agropecuária (SI), ou seja, a integração lavoura-pecuária-floresta (ILPFs), constitui uma importante estratégia de intensificação agrícola sustentável para o Brasil. Estados, como Mato Grosso (MT), tradicionalmente grandes produtores agrícolas já vem adotando esta estratégia e potencializando a sua capacidade produtiva. O Governo Federal vem, desde 2009, promovendo a disseminação e adoção dos sistemas integrados, entretanto ainda não existe uma metodologia de monitoramento desta tendência. Nossa hipótese é que técnicas de classificação Random Forest (RF) aplicadas a Séries Temporais (ST) do satélite MODIS sejam capazes de detectar determinados SI no MT. Para isso, avaliamos a acurácia do RF aplicado a ST...
Tipo: Artigo em anais de congresso (ALICE) Palavras-chave: Séries temporais; Random forest; Mato Grosso; Sistemas integrados; Sensoriamento Remoto; Remote sensing.
Ano: 2018 URL: http://www.alice.cnptia.embrapa.br/alice/handle/doc/1100305
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