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| Registros recuperados: 14 | |
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| Cremon, Cassiano; UNEMAT; Rosa Júnior, Edgard Jardim; UFMS; Serafim, Milson Evaldo; UFMS; Ono, Fábio Benedito; UFLA. |
| O objetivo deste trabalho é caracterizar micromorfometricamente os agregados de um Latossolo submetido a diferentes formas de manejo do solo. O trabalho desenvolveu-se em um LATOSSOLO VERMELHO Distroférrico, de Dourados, Mato Grosso do Sul. O delineamento experimental utilizado foi o de blocos casualizados com quatro repetições e os tratamentos foram arranjados em parcelas sub-subdivididas. Os tratamentos consistiram de dois sistemas de manejo (convencional e plantio direto), duas doses de gesso (zero e 2 t ha-1) e a utilização de duas culturas de verão (soja e milho). Os agregados foram coletados, nas profundidades de 0 a 10 e 10 a 20 cm, utilizando-se, nos procedimentos experimentais, os retidos no intervalo de diâmetro de 9,52 e 4,76 mm, após... |
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Palavras-chave: 5.02.01.07-7 Solos Florestais plantio direto; Imagens digitais; Agregados. no-tillage; Digital images; Aggregates. |
| Ano: 2009 |
URL: http://periodicos.uem.br/ojs/index.php/ActaSciAgron/article/view/6640 |
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| Ganganagowder,Narendra Veernagouda; Kamath,Priya. |
| Abstract The aim of this research is to build a supervised intelligent classification model of food products such as Biscuits, Cereals, Vegetables, Edible nuts and etc., using digital images. The Correlation-based Feature Selection (CFS) algorithm and 2nd derivative pre-treatments of the Morphological, Colour and Texture features are used to train the models for classification and detection. The best prediction accuracy is obtained for the Multilayer Perceptron (MLP), Support Vector Machines (SVM), Random Forest (RF), Simple Logistic (SLOG) and Sequential Minimal Optimization (SMO) classifiers (more than 80% of the success rate for the training/test set and 80% for the validation set). The percentage of correctly classified instances is very high in these... |
| Tipo: Info:eu-repo/semantics/article |
Palavras-chave: Algorithm; Digital images; Food classifiers; Prediction accuracy; Training/test. |
| Ano: 2017 |
URL: http://www.scielo.org.co/scielo.php?script=sci_arttext&pid=S0120-28122017000400486 |
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| Silva Júnior,Mário C. da; Pinto,Francisco de A. De C.; Queiroz,Daniel M. de; Vieira,Luciano B.; Resende,Ricardo C. de. |
| The aim of this study was to use digital images acquired by cameras attached to a helium balloon to detect variation of the nutritional status in Brachiaria decumbens. The treatments consisted of five doses of nitrogen (0, 50, 100, 150 e 200kg ha-1) with six replications each, evaluated in a completely randomized statistical design. A remote sensing system composed of digital cameras and microcomputers was used for image acquisition, and a helium balloon lifted the cameras to the heights of 15, 20, 25 and 30m. A portable chlorophyll meter and analyses of leaf nitrogen content were used to make comparisons with data obtained by the remote sensing system. Data was acquired in two phases, in different climatic conditions. At the end of each phase, dry matter... |
| Tipo: Info:eu-repo/semantics/article |
Palavras-chave: Digital images; Vegetation indices; Nitrogen. |
| Ano: 2013 |
URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0100-69162013000500016 |
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| Registros recuperados: 14 | |
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