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| Registros recuperados: 22 | |
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| Chagas,César da Silva; Vieira,Carlos Antônio Oliveira; Fernandes Filho,Elpídio Inácio. |
| Soil surveys are the main source of spatial information on soils and have a range of different applications, mainly in agriculture. The continuity of this activity has however been severely compromised, mainly due to a lack of governmental funding. The purpose of this study was to evaluate the feasibility of two different classifiers (artificial neural networks and a maximum likelihood algorithm) in the prediction of soil classes in the northwest of the state of Rio de Janeiro. Terrain attributes such as elevation, slope, aspect, plan curvature and compound topographic index (CTI) and indices of clay minerals, iron oxide and Normalized Difference Vegetation Index (NDVI), derived from Landsat 7 ETM+ sensor imagery, were used as discriminating variables. The... |
| Tipo: Info:eu-repo/semantics/article |
Palavras-chave: Terrain attributes; Neural networks; Maximum likelihood. |
| Ano: 2013 |
URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0100-06832013000200005 |
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| Sayago,S; Bocco,M. |
| Development of models for crop yield prediction using remote sensing allows accurate, reliable and timely estimations over large areas. Particularly, this information is necessary to ensure the adequacy of a nation's food supply as well as to aid policy makers and farmers. In Argentina, soybean (Glycine max (L.) Merr.) and corn (Zea mays L.) are the most important crops. The goal of this research was to develop and evaluate linear and non-linear models to estimate crop yield from satellite data. Particularly, we proposed and applied those models to obtain soybean and corn yield in the central region of Córdoba (Argentina) using Landsat and SPOT images. The models were designed taking into account all or some bands included in the images from one or both... |
| Tipo: Info:eu-repo/semantics/article |
Palavras-chave: Neural networks; Multiple linear regression; Soybean; Corn; Modelling. |
| Ano: 2018 |
URL: http://www.scielo.org.ar/scielo.php?script=sci_arttext&pid=S1668-298X2018000100001 |
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| García Cruz, Edgar. |
| En la presente investigación se analizaron imágenes digitales de hojas de frijol (Phaseolus vulgaris L.) para identificar con un clasificador, deficiencias de hierro (Fe) y manganeso (Mn). A los 24 días después de la siembra (dds) se les suministró la solución nutritiva de acuerdo a ocho tratamientos: dos deficiencias parciales, una de 50 % Fe y otra de 50 % Mn; dos deficiencias totales totales, 0 % Fe y una más de 0 % Mn además de una interacción (0 % Fe, 0 % Mn) y dos dosis excedentes (200 % Fe y 200 % Mn); finalmente un tratamiento testigo (100 % Fe, 100 % Mn) usando como referencia la solución Steiner. A partir de imágenes digitales de muestras de hojas de los tratamientos obtenidas a los 63 dds, se calcularon variables de color con los valores... |
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Palavras-chave: RGB; Textura; Redes neuronales; Phaseolus vulgaris; Hierro; Manganeso; Texture; Neural networks; Iron; Manganese; Edafología; Maestría. |
| Ano: 2013 |
URL: http://hdl.handle.net/10521/2076 |
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| Chacón,M.; Curilem,G.; Acuña,G.; Defilippi,C.; Madrid,A.M.; Jara,S.. |
| The aim of the present study was to develop a classifier able to discriminate between healthy controls and dyspeptic patients by analysis of their electrogastrograms. Fifty-six electrogastrograms were analyzed, corresponding to 42 dyspeptic patients and 14 healthy controls. The original signals were subsampled, filtered and divided into the pre-, post-, and prandial stages. A time-frequency transformation based on wavelets was used to extract the signal characteristics, and a special selection procedure based on correlation was used to reduce their number. The analysis was carried out by evaluating different neural network structures to classify the wavelet coefficients into two groups (healthy subjects and dyspeptic patients). The optimization process of... |
| Tipo: Info:eu-repo/semantics/article |
Palavras-chave: Functional dyspepsia; Electrogastrography; Wavelet transform; Neural networks. |
| Ano: 2009 |
URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0100-879X2009001200014 |
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| Reyes Flores, Maciel. |
| La detección oportuna de deficiencias nutrimentales en hojas de plantas cultivadas permite tomar medidas correctivas inmediatas asi como predecir rendimientos. Las características espectrales y de textura de las imágenes se pueden utilizar para obtener información y correlacionarlos con el estado nutrimental de elementos esenciales que generan sintomatología similar en hojas de las plantas. En la presente investigación se estableció un experimento para medir las propiedades espectrales y característica texturales del cultivo de frijol con diferentes concentraciones de nitrógeno y magnesio de imágenes obtenidas con escáner. A partir de los valores de reflectancia se generaron modelos de regresión para asociar la concentración de nitrógeno y magnesio en el... |
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Palavras-chave: Reflectancia; Discriminación; Espacios de color; Textura; Redes neuronales; Reflectance; Discrimination; Color spaces; Texture; Neural networks; Edafología; Maestría. |
| Ano: 2013 |
URL: http://hdl.handle.net/10521/2077 |
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| Joerding, Wayne H.; Li, Ying; Young, Douglas L.. |
| Feedforward networks have powerful approximation capabilities without the "explosion of parameters" problem faced by Fourier and polynomial expansions. This paper first introduces feedforward networks and describes their approximation capabilities, then we address several practical issues faced by applications of feedforward networks. First, we demonstrate networks can provide a reasonable estimate of a Bermudagrass hay fertilizer response function with the relatively sparse data often available from experiments. Second, we demonstrate that the estimated network with a practical number of hidden units provides reasonable flexibility. Third, we show how one can constrain feedforward networks to satisfy a priori information without losing their flexible... |
| Tipo: Journal Article |
Palavras-chave: Biological process models; Feedforward networks; Production function; Neural networks; Research Methods/ Statistical Methods. |
| Ano: 1994 |
URL: http://purl.umn.edu/15430 |
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| Hamm, Lonnie; Brorsen, B. Wade. |
| Neural network models were compared to traditional forecasting methods in forecasting the quarterly and monthly farm price of hogs. A quarterly neural network model forecasted poorly in comparison to a quarterly econometric model. A monthly neural network model outperformed a monthly ARIMA model with respect to the mean square error criterion and performed similarly to the ARIMA model with respect to turning point accuracy. The more positive results of the monthly neural network model in comparison to the quarterly neural network model may be due to nonlinearities in the monthly data which are not in the quarterly data. |
| Tipo: Journal Article |
Palavras-chave: Forecasting; Hog prices; Neural networks; ARIMA; Econometric; Agribusiness; Livestock Production/Industries. |
| Ano: 1997 |
URL: http://purl.umn.edu/90646 |
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| Martinez, Elodie; Brini, Anouar; Gorgues, Thomas; Drumetz, Lucas; Roussillon, Joana; Tandeo, Pierre; Maze, Guillaume; Fablet, Ronan. |
| Phytoplankton plays a key role in the carbon cycle and supports the oceanic food web. While its seasonal and interannual cycles are rather well characterized owing to the modern satellite ocean color era, its longer time variability remains largely unknown due to the short time-period covered by observations on a global scale. With the aim of reconstructing this longer-term phytoplankton variability, a support vector regression (SVR) approach was recently considered to derive surface Chlorophyll-a concentration (Chl, a proxy of phytoplankton biomass) from physical oceanic model outputs and atmospheric reanalysis. However, those early efforts relied on one particular algorithm, putting aside the question of whether different algorithms may have specific... |
| Tipo: Text |
Palavras-chave: Phytoplankton time-series reconstruction; Ocean color; Neural networks; Support vector regression; Multi-layer perceptron; Physical predictors. |
| Ano: 2020 |
URL: https://archimer.ifremer.fr/doc/00667/77871/80017.pdf |
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| Keppler, L.; Landschützer, P.; Gruber, N.; Lauvset, S. K.; Stemmler, I.. |
| The seasonal cycle represents one of the largest signals of dissolved inorganic carbon (DIC) in the ocean, yet these seasonal variations are not well established at a global scale. Here, we present the Mapped Observation‐Based Oceanic DIC (MOBO‐DIC) product, a monthly DIC climatology developed based on the DIC measurements from GLODAPv2.2019 and a two‐step neural network method to interpolate and map the measurements. MOBO‐DIC extends from the surface down to 2,000 m and from 65°N to 65°S. We find the largest seasonal amplitudes of surface DIC in the northern high‐latitude Pacific (∼30 to >50 μmol kg−1). Surface DIC maxima occur in hemispheric spring and minima in fall, driven by the input of DIC into the upper ocean by mixing during winter, and net... |
| Tipo: Text |
Palavras-chave: DIC; Seasonal variability; Neural networks; SOM‐ FFN; Monthly climatology; NCP. |
| Ano: 2020 |
URL: https://archimer.ifremer.fr/doc/00668/78016/80262.pdf |
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| Registros recuperados: 22 | |
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