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Registros recuperados: 45 | |
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GALÃO, O. F.; BORSATO, D.; PINTO, J. P.; VISENTAINER, J. V.; CARRÃO-PANIZZI, M. C.. |
Vinte variedades de soja (Glycine max), quatorze convencionais e seis variedades transgênicas (RR) foram analisadas quanto ao teor de proteína, ácido fítico, teor de óleo, fitosteróis, cinzas, minerais e ácidos graxos que foram tabelados e apresentados à rede neural do tipo perceptron de múltiplas camadas para a classificação e identificação quanto a região de plantio e quanto a variedade convencional ou transgênica. A rede neural utilizada classificou e testou corretamente 100% das amostras cultivadas por região. Para o banco de dados contendo informações sobre sojas transgênicas e convencionais foi obtido um desempenho de 94,43% no treinamento da rede, 83,30% no teste e 100% na validação. Twenty soybean (Glycine max) varieties, 14 conventional and 6... |
Tipo: Separatas |
Palavras-chave: Variedade.; Soja; Neural networks; Soybeans; Varieties.. |
Ano: 2011 |
URL: http://www.alice.cnptia.embrapa.br/alice/handle/doc/898723 |
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CHAGAS, C. da S.; VIEIRA, C. A. O.; FERNANDES-FILHO, E. I.. |
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: Artigo de periódico |
Palavras-chave: Terrain attributes; Maximum likelihood; Neural networks. |
Ano: 2013 |
URL: http://www.alice.cnptia.embrapa.br/alice/handle/doc/958086 |
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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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BARBEDO, J. G. A.; KOENIGKAN, L. V.; SANTOS, P. M.; RIBEIRO, A. R. B.. |
Abstract: The management of livestock in extensive production systems may be challenging, especially in large areas. Using Unmanned Aerial Vehicles (UAVs) to collect images from the area of interest is quickly becoming a viable alternative, but suitable algorithms for extraction of relevant information from the images are still rare. This article proposes a method for counting cattle which combines a deep learning model for rough animal location, color space manipulation to increase contrast between animals and background, mathematical morphology to isolate the animals and infer the number of individuals in clustered groups, and image matching to take into account image overlap. Using Nelore and Canchim breeds as a case study, the proposed approach yields... |
Tipo: Artigo de periódico |
Palavras-chave: Redes neurais; Rede neural convolucional; Veículo aéreo não tripulado; Canchim breed; Nelore breed; Convolutional neural networks; Mathematical morphology; Deep learning mode; Gado de Corte; Gado Nelore; Gado Canchim; Unmanned aerial vehicles; Neural networks. |
Ano: 2020 |
URL: http://www.alice.cnptia.embrapa.br/alice/handle/doc/1121664 |
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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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GONÇALVES, J. P.; PINTO, F. A. C.; QUEIROZ, D. M.; VILLAR, F. M. M.; BARBEDO, J. G. A.; DEL PONTE, E. M.. |
Colour-thresholding digital imaging methods are generally accurate for measuring the percentage of foliar area affected by disease or pests (severity), but they perform poorly when scene illumination and background are not uniform. In this study, six convolutional neural network (CNN) architectures were trained for semantic segmentation in images of individual leaves exhibiting necrotic lesions and/or yellowing, caused by the insect pest coffee leaf miner (CLM), and two fungal diseases: soybean rust (SBR) and wheat tan spot (WTS). All images were manually annotated for three classes: leaf background (B), healthy leaf (H) and injured leaf (I). Precision, recall, and Intersection over Union (IoU) metrics in the test image set were the highest for B, followed... |
Tipo: Artigo de periódico |
Palavras-chave: Aprendizado profundo; Fitopatometria; Inteligência artificial; Aprendizado de máquina; Rede neural convolucional; Segmentação de imagem; Phytopathometry; Machine learning; Convolutional neural network; Image segmentation; Doença de Planta; Artificial intelligence; Plant diseases and disorders; Neural networks. |
Ano: 2021 |
URL: http://www.alice.cnptia.embrapa.br/alice/handle/doc/1134326 |
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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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SANTOS, A. A. dos; AVILA, S.; SANTOS, T. T.. |
RESUMO - Neste trabalho, o problema de detecção de frutas e folhas em viticultura para aplicações envolvendo sensoriamento próximo foi modelado como um problema de aprendizado supervisionado de máquina. Uma base de dados foi criada e manualmente anotada a partir de imagens obtidas em abril de 2017 na Vinícola Guaspari. No total são 11.883 imagens contendo exemplos de cachos de uvas e folhas. Uma rede convolutiva com arquitetura YOLOv2 foi treinada para localização e classificação de cachos e folhas. Testes quantitativos demonstraram resultados para a detecção e classificação com precisão de 100%, revocação de até 74,2% e F1-Score de 85,2% para classe "uva" e precisão de 100%, revocação de até 67,9% e F1-Score de 80,9% para a classe "folha". Testes... |
Tipo: Anais e Proceedings de eventos |
Palavras-chave: Detecção de frutos; Reconhecimento de Imagens; Aprendizagem profunda; Aprendizado de máquina; Redes neurais; Aprendizado supervisionado; Image Recognition; Fruit detection; Deep Learning; Learning machine; Viticultura; Viticulture; Neural networks. |
Ano: 2018 |
URL: http://www.alice.cnptia.embrapa.br/alice/handle/doc/1096173 |
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SOUZA, L. L. de; AVILA, S.; SANTOS, T. T.. |
RESUMO - Neste trabalho investigamos técnicas de detecção de objetos por redes neurais aplicadas à detecção de frutos em viticultura. Desenvolvemos também a base de dados Embrapa WGISD, composta por imagens coletadas em Abril de 2017 e Abril de 2018 na Vinícola De Guaspari. Anotada manualmente, a base de dados possui 5 cultivares diferentes de uva: Syrah, Chardonnay, Cabernet Franc, Cabernet Sauvignon e Sauvignon Blanc, totalizando 4419 amostras de cachos de uva. Foram treinadas duas redes neurais convolutivas de arquiteturas, YOLOv2 e YOLOv3, para detecção e localização dos cachos nas imagens. Resultados quantitativos demonstraram precisão de até 88%, revocação de até 74%, e F1-Score de até 80% para YOLOv2 e precisão de até 92%, revocação de até 65% e... |
Tipo: Anais e Proceedings de eventos |
Palavras-chave: Detecção de frutos; Redes neurais; Aprendizagem profunda; Detecção de uvas; Fruit detection; Deep Learning; Viticultura; Viticulture; Neural networks. |
Ano: 2019 |
URL: http://www.alice.cnptia.embrapa.br/alice/handle/doc/1111590 |
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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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Registros recuperados: 45 | |
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